Shopping mall store recommendation method, device, equipment and medium

Abstract

The present application relates to a method, apparatus, device and medium for recommending stores in a shopping mall. The method comprises: using a long short-term memory network model trained with user behavior time series data to predict the stores that a target old user is interested in during the current period; using a preset collaborative filtering algorithm to calculate and determine the second similarity between the target old user and other users based on the stores that the target old user is interested in during the current period, so as to determine the target old user's neighbor users; predicting the target old user's estimated rating for the stores that the target old user has not rated based on the ratings of the neighbor users for each store; calculating and determining the corresponding second recommendation degree for each store based on the estimated rating, the actual distance between the target old user and the store, and the comprehensive recommendation value; and displaying each store to the target old user's user interface from high to low according to the second recommendation degree, so as to complete the store recommendation for the shopping mall. The present application can improve the traffic and user stickiness of the shopping mall.

Description

Technical Field

[0001] The present application relates to the field of product recommendation, and in particular to a method for recommending stores in a shopping mall, a corresponding device, an electronic device, and a computer-readable storage medium. Background Art

[0002] With the rapid development of information technology, recommendation systems and path planning algorithms play a vital role in helping users make decisions and process large amounts of information. However, these technologies still face some challenges in practical applications.

[0003] The cold start problem is particularly prominent for new users, as they lack historical interaction data. This makes it difficult for the system to build an accurate user profile and, consequently, provide personalized recommendations. Furthermore, new users' interests can change rapidly over a short period of time, requiring recommendation systems to quickly adapt to these changes and provide timely recommendations. Furthermore, due to a lack of sufficient interaction data, interactions between new users and items are very sparse, making it even more difficult to capture their true preferences.

[0004] For existing users, as the number of users and the variety of items increases, interactions between users and items become increasingly sparse, leading to incomplete data. This sparsity makes it difficult for the system to accurately capture users' true preferences. Furthermore, existing users' interests may change over time, in different environments, and through their personal experiences. Recommendation systems must be able to update in real time to respond to these changes. As the amount of data increases, the computational complexity of the algorithm also increases, which not only affects the system's responsiveness but also limits its scalability.

[0005] When it comes to path planning, the system must balance multiple objectives, such as minimizing time, minimizing cost, and maximizing comfort. This complicates the optimization process. Obstacles and path variations in real-world environments require path planning algorithms to be updated in real time to adapt to these changes. Furthermore, traditional path planning algorithms may not accurately reflect the user's actual travel path and distance, necessitating more precise algorithms to provide optimized paths.

[0006] To sum up, the existing technology adapts to the fact that the interaction between new users and items is very sparse, which makes it more difficult to capture the user's true preferences. For old users, as the number of users and the types of items increase, the interaction between users and items becomes increasingly sparse, making it difficult for the system to accurately capture the user's true preferences. In order to solve this problem, the applicant has made corresponding explorations. Summary of the Invention

[0007] The purpose of this application is to solve the above problems and provide a shopping mall store recommendation method, corresponding device, electronic device and computer-readable storage medium.

[0008] In order to meet the various objectives of this application, this application adopts the following technical solutions:

[0009] A method for recommending stores in a shopping mall, which is proposed to meet one of the purposes of this application, includes:

[0010] Obtain the number of store reviews and the store comprehensive score corresponding to each store in the shopping mall, determine the store popularity value corresponding to the store based on the number of store reviews and the store comprehensive score, determine the store popularity corresponding to the store based on the store popularity value, the store comprehensive score and the number of new reviews of the store within a preset time period, and determine the comprehensive recommendation value corresponding to each store based on the store popularity value and the store popularity;

[0011] In response to an instruction to recommend a store to a target new user, obtaining a user interest category tag of the target new user and a store category tag corresponding to each store, calculating and determining a first similarity between the user interest category tag and the store category tag, determining a first recommendation degree corresponding to each store based on the comprehensive recommendation value, the first similarity, and the actual distance between the target new user and the store, and displaying each store on a user interface of the target new user in descending order of the first recommendation degree;

[0012] In response to an instruction to recommend a store to an old user, obtain user behavior time series data of the target old user, wherein the user behavior time series data includes historical consumption records and user consumption levels. The historical consumption records represent information about stores visited and consumed by the user in different time periods. The user consumption level is obtained by the user's consumption frequency at different stores and the store's price range;

[0013] A long short-term memory network model trained with the user behavior time series data is used to predict the target former user's stores of interest in the current time period, and a preset collaborative filtering algorithm is used to calculate and determine a second similarity between the target former user and other users based on the target former user's stores of interest in the current time period, so as to determine the target former user's neighboring users;

[0014] Based on the ratings of the neighbor users on each store, the estimated ratings of the target old user on the stores that have not been rated by the target old user are predicted. The second recommendation degree corresponding to each store is calculated and determined based on the estimated ratings, the actual distance between the target old user and the store, and the comprehensive recommendation value. Each store is displayed to the user interface of the target old user from high to low according to the second recommendation degree to complete the store recommendation of the shopping mall.

[0015] Optionally, the store popularity value corresponding to the store is determined according to the number of store reviews and the store comprehensive score. The calculation formula of the store popularity value is:

[0016]

[0017] Among them, Hotness represents the store popularity value corresponding to each store, R1 represents the number of store reviews received by the store, and r represents the store's comprehensive score;

[0018] The store popularity of the store is determined based on the store popularity value, the store comprehensive score, and the number of new reviews of the store within a preset time period. The calculation formula of the store popularity is:

[0019] P=α·Hotness+β·S+γ·ΔN,

[0020] Where P represents the store popularity of each store, S represents the store's comprehensive score, ΔN represents the number of new reviews of the store within a preset time range, and α, β, and γ represent weight coefficients;

[0021] The comprehensive recommendation value corresponding to each store is determined based on the store heat value and the store popularity. The calculation formula of the comprehensive recommendation value is:

[0022] R=λ·P+(1-λ)·Hotness,

[0023] Among them, R is the comprehensive recommendation value corresponding to each store, and λ is the weight coefficient, which is used to balance the importance of store popularity and store heat value.

[0024] Optionally, a first similarity between the user's interested category tag and the store category tag is calculated and determined, and the calculation formula for the first similarity is:

[0025]

[0026] Among them, Jaccard similarity represents the first similarity, A is the user's interested category label set, B is the store category label set, and the higher the Jaccard similarity, the greater the user's interest in the store;

[0027] The first recommendation degree corresponding to each store is determined based on the comprehensive recommendation value, the first similarity, and the actual distance between the target new user and the store. The calculation formula of the first recommendation degree is:

[0028]

[0029] Among them, ω1 represents the weight of the comprehensive recommendation value, ω2 represents the weight of the Jaccard similarity, ω3 represents the weight of the actual distance between the target new user and the store, and R represents the comprehensive recommendation value corresponding to each store.

[0030] Optionally, the calculation formula for the user consumption level is:

[0031]

[0032] Among them, T i represents the number of times or frequency of consumption by the user in the i-th store, P i represents the price range of the store, and X represents the user's consumption level.

[0033] Optionally, a preset collaborative filtering algorithm is used to calculate and determine a second similarity between the target old user and other users based on the stores that the target old user is interested in during the current period. The calculation formula for the second similarity is expressed as:

[0034]

[0035] Among them, sim(u,v) represents the second similarity, u represents the vector of the target old user, and v represents the vector of other users. u and v contain information about the user's interest in the store and consumption level. θ represents the angle between user vectors. The value range of cosine similarity cos(θ) is [0,1]. The larger the value, the more similar the other users are to the target old user.

[0036] Optionally, the estimated ratings of the target old user for the unrated stores are predicted based on the ratings of the neighbor users for each store. The calculation formula for the estimated ratings is:

[0037]

[0038] in, is the estimated rating value, which represents the predicted rating of the target old user u on the store i, N represents the set of neighboring users with the highest similarity to the target old user u, ω uv represents the similarity between the target old user u and the neighbor user v, r vi represents the rating of store i by neighbor user v;

[0039] The second recommendation corresponding to each store is calculated based on the estimated rating value, the actual distance between the target old user and the store, and the comprehensive recommendation value. The calculation formula for the second recommendation is:

[0040]

[0041] Among them, α, β and γ represent weight coefficients, and R represents the comprehensive recommendation value corresponding to each store.

[0042] Optionally, the step of determining the actual distance between the user and the store includes:

[0043] Obtain a shopping mall map to determine the starting location of target new or existing users and the locations of multiple target stores;

[0044] An improved Dijkstra algorithm is used to perform path planning based on the starting location and multiple target store locations to determine the actual distance that the target new user or the target old user needs to travel from the current location to each target store.

[0045] A shopping mall store recommendation device provided for another purpose of the present application includes:

[0046] A comprehensive recommendation value determination module is configured to obtain the number of store reviews and the store comprehensive score corresponding to each store in the shopping mall, determine the store popularity value corresponding to the store based on the number of store reviews and the store comprehensive score, determine the store popularity corresponding to the store based on the store popularity value, the store comprehensive score, and the number of new reviews of the store within a preset time period, and determine the comprehensive recommendation value corresponding to each store based on the store popularity value and the store popularity;

[0047] A new user store recommendation module is configured to respond to an instruction to recommend a store to a target new user, obtain a user interest category tag of the target new user and a store category tag corresponding to each store, calculate and determine a first similarity between the user interest category tag and the store category tag, determine a first recommendation degree corresponding to each store based on the comprehensive recommendation value, the first similarity, and the actual distance between the target new user and the store, and display each store on the user interface of the target new user in descending order of the first recommendation degree;

[0048] A user behavior data acquisition module is configured to respond to an instruction to recommend stores to an old user and acquire user behavior time series data of a target old user, wherein the user behavior time series data includes historical consumption records and user consumption levels. The historical consumption records represent information about stores visited and consumed by the user in different time periods. The user consumption level is obtained by the user's consumption frequency at different stores and the store's price range;

[0049] a neighbor user determination module configured to use a long short-term memory network model trained using the user behavior time series data to predict the target former user's stores of interest in the current time period, and to use a preset collaborative filtering algorithm to calculate and determine a second similarity between the target former user and other users based on the target former user's stores of interest in the current time period, so as to determine the target former user's neighbor users;

[0050] The old user store recommendation module is configured to predict the estimated rating value of the target old user for the unrated stores based on the rating values ​​of the neighbor users for each store, calculate and determine the second recommendation degree corresponding to each store based on the estimated rating value, the actual distance between the target old user and the store, and the comprehensive recommendation value, and display each store to the user interface of the target old user from high to low according to the second recommendation degree to complete the store recommendation of the shopping mall.

[0051] An electronic device provided to meet another purpose of the present application includes a central processing unit and a memory, wherein the central processing unit is used to call and run a computer program stored in the memory to execute the steps of the shopping mall store recommendation method described in the present application.

[0052] A computer-readable storage medium is provided to meet another purpose of the present application, which stores a computer program implemented according to the shopping mall store recommendation method in the form of computer-readable instructions. When the computer program is called and executed by a computer, it executes the steps included in the corresponding method.

[0053] Compared with the existing technology, this application addresses the following issues: the interaction between new users and items is very sparse, which makes it more difficult to capture the user's true preferences. For old users, as the number of users and the variety of items increases, the interaction between users and items becomes increasingly sparse, making it difficult for the system to accurately capture the user's true preferences. This application includes but is not limited to the following beneficial effects:

[0054] First, by using an improved Dijkstra algorithm, the system can accurately calculate the actual walking distance of users to each store, taking into account the user's actual walking path rather than just traditional distance metrics (such as Manhattan distance or Euclidean distance). This precise path planning greatly improves the user's navigation experience in the shopping mall, provides users with the optimal walking route, and further optimizes the shopping process.

[0055] Secondly, this application solves the cold start problem for new users. By combining user-selected tags with the characteristics of popular stores in the current shopping mall, the system can effectively alleviate the cold start problem. The Jaccard similarity coefficient is used to analyze the relationship between user-selected category tags and store category tags, allowing new users to quickly discover stores that match their potential interests.

[0056] Third, for existing users, the system uses an LSTM neural network model to analyze historical user behavior data, accurately capturing their interests. Combined with a clustering method based on segmented categories, this reduces sparsity issues, improving recommendation accuracy and data processability. By weighting the factors in the user-item matrix, the system can more accurately reflect user preferences for each store, enhancing the level of personalized recommendations.

[0057] Fourthly, this application utilizes a user similarity rating prediction method to more comprehensively predict a user's interest in a store. When a user lacks direct interaction history with a store, the system can use the ratings and feedback of neighboring users to predict the target user's potential preferences. This weighted prediction method based on neighboring users effectively fills gaps in the user-item matrix, further improving the accuracy and diversity of recommendations.

[0058] Fifth, at the core of our recommendation algorithm, the system comprehensively considers data from multiple dimensions (such as store ratings, number of reviews, and newly added reviews) and calculates a store's popularity and buzz using a weighted average model. This multi-dimensional, comprehensive calculation ensures the accuracy and timeliness of our recommendations, ensuring they better align with user interests and needs.

[0059] Sixth, by analyzing data such as the price range and frequency of visits to stores, the system can more accurately build user profiles, analyze their spending levels, and provide a more reliable basis for personalized recommendations. Computing similarities between users further enhances the system's recommendation capabilities, ensuring that each user receives recommendations that best meet their needs.

[0060] Seventh, this application improves user satisfaction and shopping experience. Combining precise route planning, real-time updates, and personalized recommendations, the system can significantly enhance the user shopping experience, reduce unnecessary time waste, and help users quickly discover stores that match their interests. Overall, the recommendation system can significantly increase user satisfaction, boosting shopping mall traffic and user retention.

[0061] In summary, this application implements a recommendation system that comprehensively utilizes user behavior data, store characteristics, and actual distance. By combining deep learning models with multiple algorithms, it provides users with personalized store recommendations, significantly improving the user experience and increasing customer satisfaction. This technical solution not only has good practicality, but also contributes to the construction of future smart shopping navigation systems, adding more convenience and fun to the user's shopping experience. Combining positioning technology, real-time data analysis, and personalized recommendation algorithms, this application demonstrates broad application prospects and can provide strong support for future smart retail and shopping center management. BRIEF DESCRIPTION OF THE DRAWINGS

[0062] The above and / or additional aspects and advantages of the present application will become apparent and easily understood from the following description of the embodiments in conjunction with the accompanying drawings, in which:

[0063] Figure 1 This is a flowchart of a method for recommending stores in a shopping mall in an embodiment of the present application;

[0064] Figure 2 This is a flow chart of the broken line turning shopping mall route planning diagram in the embodiment of the present application;

[0065] Figure 3 This is a flow chart of the circular shopping mall route planning diagram in the embodiment of the present application;

[0066] Figure 4 This is a schematic diagram of the process of polygonal shopping mall path planning in an embodiment of the present application;

[0067] Figure 5 Schematic diagram of the comparison of training time among the RNN model, LSTM model, and GRU model in the embodiment of the present application;

[0068] Figure 6 This is a schematic diagram comparing the inference time of the RNN model, LSTM model, and GRU model in the embodiments of the present application;

[0069] Figure 7 Schematic diagram of the performance comparison of the RNN model, LSTM model and GRU model in terms of training loss in the embodiment of the present application;

[0070] Figure 8 This is a schematic diagram showing the performance comparison of the RNN model, LSTM model, and GRU model in terms of verification loss in an embodiment of the present application;

[0071] Figure 9 Schematic diagram of the performance comparison of the RNN model, LSTM model and GRU model in terms of accuracy in the embodiment of the present application;

[0072] Figure 10This is a functional block diagram of a device for recommending stores in a shopping mall in an embodiment of the present application;

[0073] Figure 11 Schematic diagram of the structure of the computer device in the embodiment of the present application. DETAILED DESCRIPTION

[0074] The following describes in detail embodiments of the present application, examples of which are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present application, and are not to be construed as limiting the present application.

[0075] It will be understood by those skilled in the art that, unless expressly stated otherwise, the singular forms "a", "an", "said" and "the" used herein may also include the plural forms. It should be further understood that the term "comprising" used in the specification of the present application refers to the presence of the features, integers, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components and / or groups thereof. It should be understood that when we refer to an element as being "connected" or "coupled" to another element, it may be directly connected or coupled to the other element, or there may be intermediate elements. In addition, "connected" or "coupled" as used herein may include wireless connections or wireless couplings. The term "and / or" used herein includes all or any units and all combinations of one or more associated listed items.

[0076] It will be understood by those skilled in the art that, unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by those skilled in the art to which this application belongs. It should also be understood that terms such as those defined in common dictionaries should be understood to have meanings consistent with their meanings in the context of the prior art and will not be interpreted in an idealized or overly formal sense unless specifically defined as herein.

[0077] As will be understood by those skilled in the art, the terms "client," "terminal," and "terminal device" as used herein include both devices that are wireless signal receivers, i.e., devices that only have wireless signal receivers without transmission capabilities, and devices that have receiving and transmitting hardware capable of two-way communication over a two-way communication link. Such devices may include: cellular or other communication devices such as personal computers and tablet computers, which have single-line displays, multi-line displays, or cellular or other communication devices without multi-line displays; PCS (Personal Communications Services), which may combine voice, data processing, fax, and / or data communication capabilities; PDAs (Personal Digital Assistants), which may include a radio frequency receiver, a pager, Internet / Intranet access, a web browser, a notepad, a calendar, and / or a GPS (Global Positioning System) receiver; and conventional laptop and / or palmtop computers or other devices, which have and / or include a radio frequency receiver. As used herein, the terms "client," "terminal," or "terminal device" may be portable, transportable, or installed in a vehicle (air, sea, and / or land), or may be adapted and / or configured to operate locally and / or in a distributed manner at any other location on Earth and / or in space. As used herein, the terms "client," "terminal," or "terminal device" may also refer to a communication terminal, an Internet terminal, or a music / video playback terminal, such as a PDA, an MID (Mobile Internet Device), and / or a mobile phone with music / video playback capabilities, or may include a smart TV, a set-top box, or other device.

[0078] The hardware referred to by names such as "server", "client", and "service node" in this application is essentially an electronic device with capabilities equivalent to those of a personal computer. It is a hardware device that has the necessary components revealed by the von Neumann principle, such as a central processing unit (including an arithmetic unit and a controller), a memory, an input device, and an output device. Computer programs are stored in its memory, and the central processing unit loads the program stored in the external memory into the internal memory for execution, executes the instructions in the program, and interacts with the input and output devices to complete specific functions.

[0079] It should be noted that the concept of "server" referred to in this application can also be extended to server clusters. Based on the network deployment principles understood by those skilled in the art, the servers described should be logically divided. In physical space, these servers can be independent of each other but callable through interfaces, or integrated into a single physical computer or a computer cluster. Those skilled in the art should understand this flexibility and should not use it to constrain the implementation of the network deployment method of this application.

[0080] Unless expressly specified, one or more technical features of the present application can be deployed on a server for implementation and accessed by a client through a remote call to obtain an online service interface provided by the server, or can be directly deployed and run on a client for implementation.

[0081] Unless expressly specified otherwise, the neural network models referenced or may be referenced in this application may be deployed on a remote server and remotely called on the client, or may be deployed and directly called on a client with sufficient device capabilities. In some embodiments, when it runs on the client, its corresponding intelligence may be obtained through transfer learning to reduce the requirements for the client's hardware operating resources and avoid excessive occupation of the client's hardware operating resources.

[0082] Unless explicitly specified, the various data involved in this application can be stored remotely on a server or on a local terminal device, as long as they are suitable for being called by the technical solution of this application.

[0083] Those skilled in the art should be aware that although the various methods of this application are described based on the same concept and thus exhibit commonality, unless otherwise specified, these methods can be independently executed. Similarly, the various embodiments disclosed in this application are all based on the same inventive concept. Therefore, concepts with the same expression, as well as concepts that are appropriately transformed for convenience despite different expression, should be understood as equivalent.

[0084] Unless expressly stated to be mutually exclusive, the various embodiments disclosed in this application may be cross-combined with the relevant technical features of the various embodiments to flexibly construct new embodiments, as long as such combination does not deviate from the creative spirit of this application and can meet the needs of the prior art or resolve certain deficiencies in the prior art. Those skilled in the art should be aware of such flexibility.

[0085] See also Figure 1 In one embodiment, the shopping mall store recommendation method of the present application includes:

[0086] Step S10: Obtain the number of store reviews and the store comprehensive score corresponding to each store in the shopping mall, determine the store popularity value corresponding to the store based on the number of store reviews and the store comprehensive score, determine the store popularity corresponding to the store based on the store popularity value, the store comprehensive score, and the number of new reviews of the store within a preset time period, and determine the comprehensive recommendation value corresponding to each store based on the store popularity value and the store popularity;

[0087] The shopping mall store recommendation system can obtain the number of store reviews and the store comprehensive score corresponding to each store in the shopping mall, determine the store popularity value corresponding to the store based on the number of store reviews and the store comprehensive score, determine the store popularity corresponding to the store based on the store popularity value, the store comprehensive score and the number of new reviews of the store within a preset time period, and determine the comprehensive recommendation value corresponding to each store based on the store popularity value and the store popularity;

[0088] Specifically, in terms of constructing a multi-metric dataset, the implementation process first involves the systematic collection of store information. The shopping mall store recommendation system utilizes public databases to obtain information such as the overall store rating, number of reviews, and the number of new reviews in the past month, providing the basic data for the recommendation system. This data not only supports subsequent recommendations but also enables the system to conduct comprehensive data analysis. Simultaneously, the system obtains user-authorized shopping history and behavioral data in the shopping mall to ensure data accuracy and legitimacy. After obtaining this data, the system conducts in-depth analysis to understand user preferences and habits, laying the foundation for personalized recommendations.

[0089] Data preprocessing is a critical step in ensuring data quality. Collected data needs to be cleaned, denoised, and standardized to improve its quality and usability. Specifically, data cleaning involves removing duplicates, filling in missing values, and addressing outliers to prevent inaccurate data from negatively impacting recommendation results. Furthermore, standardization unifies data from different formats and sources to enable efficient processing by subsequent algorithms, ensuring data integrity and consistency. To this end, the system utilizes advanced data processing techniques and tools to ensure data quality.

[0090] In some embodiments, information such as the comprehensive rating, number of reviews, and number of new reviews in the past month for each store is systematically collected from public databases and stored in a data warehouse. The collected data is cleaned to remove duplicates, erroneous information, and missing values. After data cleaning, it is standardized to ensure data consistency and reliability. Based on the collected store information, the store heat value and store popularity value of each store are calculated: the heat value is processed by logarithmically scaling the number of store reviews, and the calculation formula is:

[0091] Store popularity value = log (number of comments + 1),

[0092] Store popularity can be calculated using a weighted average model, combining comprehensive ratings, the number of reviews, and the number of new reviews. User behavior data in shopping malls, including store visit frequency and purchase history, is collected with user authorization to ensure data legitimacy and privacy. Multi-metric datasets are updated regularly (e.g., weekly or monthly) to ensure the recommendation system reflects changes in users and stores in real time, maintaining system accuracy and effectiveness.

[0093] In a specific embodiment, the store popularity value corresponding to the store is determined according to the number of store reviews and the store comprehensive score. The calculation formula of the store popularity value is:

[0094]

[0095] Among them, Hotness represents the store popularity value corresponding to each store, R1 represents the number of store reviews received by the store, and r represents the store's comprehensive score;

[0096] The store popularity of the store is determined based on the store popularity value, the store comprehensive score, and the number of new reviews of the store within a preset time period. The calculation formula of the store popularity is:

[0097] P=α·Hotness+β·S+γ·ΔN,

[0098] Where P represents the store popularity of each store, S represents the store's comprehensive score, ΔN represents the number of new reviews of the store within a preset time range, and α, β, and γ represent weight coefficients;

[0099] The comprehensive recommendation value corresponding to each store is determined based on the store heat value and the store popularity. The calculation formula of the comprehensive recommendation value is:

[0100] R=λ·P+(1-λ)·Hotness,

[0101] Among them, R is the comprehensive recommendation value corresponding to each store, and λ is the weight coefficient, which is used to balance the importance of store popularity and store heat value.

[0102] Step S20: In response to an instruction to recommend a store to a target new user, obtaining a user interest category tag of the target new user and a store category tag corresponding to each store, calculating and determining a first similarity between the user interest category tag and the store category tag, determining a first recommendation degree corresponding to each store based on the comprehensive recommendation value, the first similarity, and the actual distance between the target new user and the store, and displaying each store on the user interface of the target new user in descending order of the first recommendation degree;

[0103] After determining the comprehensive recommendation value corresponding to each store based on the store heat value and the store popularity, the shopping mall store recommendation system can respond to an instruction to recommend a store to a target new user, obtain the user interest category tag of the target new user and the store category tag corresponding to each store, calculate and determine a first similarity between the user interest category tag and the store category tag, determine a first recommendation degree corresponding to each store based on the comprehensive recommendation value, the first similarity, and the actual distance between the target new user and the store, and display each store in a descending order of the first recommendation degree to the user interface of the target new user;

[0104] Specifically, during the application of the recommendation algorithm, the system constructs a user-store matrix to match user preferences with store categories within the shopping mall. For new users, the system presents a series of store category options when they first enter the platform. These options are used to generate category labels for the user, facilitating subsequent use of the recommendation algorithm. A content-based recommendation algorithm, incorporating multiple metrics, then analyzes the similarity between these labels and the store's category labels, evaluating the recommendation by calculating the Jaccard similarity. This approach not only effectively alleviates the cold start problem for new users but also helps them quickly discover potential stores of interest.

[0105] In some embodiments, when a new user registers or logs in for the first time, basic information such as age, gender, shopping frequency, preferred product types, etc. is collected through a questionnaire or selection box, and this information is stored in a user database for subsequent construction of a user profile.

[0106] The shopping mall store recommendation system first performs a cluster analysis of all the stores in the shopping mall by subdividing their categories. This step determines the type and number of clusters based on the subdivided categories of all the stores in the shopping mall, aiming to divide the stores into broader categories to make it easier for new users to choose. After the clustering is completed, the system displays these store categories (for example: clothing, food, entertainment, home furnishings, etc.) on the user's login interface in the form of icons and text, allowing users to select the category of interest. The system records the user's selection and converts it into a category label to facilitate subsequent similarity calculations.

[0107] Furthermore, the user's category selection tags are generated, and the user's selected categories are mapped to a set of keywords, such as "clothing", "food", etc. These tags will be used in the subsequent similarity calculation and recommendation process.

[0108] In a specific embodiment, a first similarity between the user's interested category tag and the store category tag is calculated and determined. The calculation formula for the first similarity is:

[0109]

[0110] Among them, Jaccard similarity represents the first similarity, A is the user's interested category label set, B is the store category label set, and the higher the Jaccard similarity, the greater the user's interest in the store;

[0111] The first recommendation degree corresponding to each store is determined based on the comprehensive recommendation value, the first similarity, and the actual distance between the target new user and the store. The calculation formula of the first recommendation degree is:

[0112]

[0113] Among them, ω1 represents the weight of the comprehensive recommendation value, ω2 represents the weight of the Jaccard similarity, ω3 represents the weight of the actual distance between the target new user and the store, and R represents the comprehensive recommendation value corresponding to each store.

[0114] In some embodiments, the step of determining the actual distance between the user and the store includes:

[0115] Step S201: Obtain a shopping mall map to determine the starting location of a target new user or a target old user and the locations of multiple target stores;

[0116] Step S202: Using an improved Dijkstra algorithm to perform path planning based on the starting location and multiple target store locations, to determine the actual distance from the current location of the target new user or the target old user to each target store.

[0117] Specifically, an improved Dijkstra algorithm is used to implement multi-objective path planning. This algorithm can quickly calculate and automatically display the actual walking distance of users to each store in the shopping mall. The system first automatically locates the user's current location. This process does not require user intervention, ensuring a convenient user experience. Subsequently, the system imports detailed map data from the shopping mall's public database, including the precise coordinates of each store, areas where customers can move freely, and the locations of obstacles that may affect customers' walking. Using this data, combined with indoor positioning technology, the system can accurately determine the user's current location. Based on the user's current location, the improved Dijkstra algorithm is applied to calculate the shortest path from the user to each store.

[0118] The algorithm is specifically designed to handle multi-objective path planning problems, improving efficiency by maintaining a priority queue to ensure that the system always prioritizes the stores closest to the user. When calculating the path, the algorithm takes into account the path length, estimated walking time, and the user's personal preferences to ensure that the most optimized walking route is provided. In addition, the system displays the calculated actual walking distance in real time next to each store entry, providing users with instant reference information. This real-time update and display of the real-time actual distance of each store relative to the user not only provides accurate actual walking distance, but also dynamically adjusts the recommendation list to ensure that users see the most relevant store options based on the actual distance indicator.

[0119] During implementation, the system first models the environment, constructs a mobility model for the service recipient, and generates an obstacle grid map, simulating the actual layout of a shopping mall. During path planning, the system inputs the coordinates of the user's starting point and target store into an algorithm, initializing a set of nodes to be visited and a set of nodes already visited. By looping through these nodes, the system continuously updates and optimizes the path until all target nodes have been visited.

[0120] See also Figures 2 to 4 The algorithm not only focuses on the shortest path but also considers the user's movement pattern in real time, updating the path appropriately to account for unexpected obstacles or changes in the path. For example, if the system detects a new obstacle in the user's path, it immediately recalculates the optimal path to ensure the user reaches their destination smoothly. This algorithm is designed to provide an optimal shopping experience, significantly reducing the time and inconvenience of walking within the mall.

[0121] In some embodiments, a floor plan of a shopping mall can be imported from a public database, containing the coordinates of each store, accessible areas, and location information of obstacles. Ensure that the data format supports the input of the path planning algorithm. Set the user's starting point (current location) and the coordinates of the target store, call the improved Dijkstra algorithm, calculate the user's actual walking path to each target store, and record the shortest path and corresponding distance of each store for subsequent recommendation; while the user is walking, monitor their current location in real time, obtain location data through sensors or mobile devices, and immediately replan the path if new obstacles or path changes are detected to ensure that the user can get the latest navigation information; display the calculated path to the user in a graphical manner through the mobile device interface, including walking routes, turn prompts, and estimated arrival time, to enhance the user's shopping experience.

[0122] It can be seen from the above embodiments that the first recommendation degree corresponding to each store can be determined based on the comprehensive recommendation value corresponding to each store, the first similarity and the actual distance between the target new user and the store, and each store can be displayed to the user interface of the target new user from high to low according to the first recommendation degree.

[0123] Step S30: In response to the instruction to recommend a store to the old user, obtain the user behavior time series data of the target old user, wherein the user behavior time series data includes historical consumption records and user consumption levels. The historical consumption records represent the store information visited and consumed by the user in different time periods. The user consumption level is obtained by the user's consumption frequency in different stores and the store's price range;

[0124] The shopping mall store recommendation system can respond to the instruction to recommend stores to old users and obtain the user behavior time series data of the target old users, wherein the user behavior time series data includes historical consumption records and user consumption levels. The historical consumption records represent the store information visited and consumed by the user in different time periods. The user consumption level is obtained by the user's consumption frequency in different stores and the store's price range;

[0125] Specifically, for old users, the shopping mall store recommendation system adopts a collaborative filtering recommendation algorithm that integrates the long short-term memory network model (LSTM model). First of all, the LSTM model has a strong ability in time series data processing and can capture the dynamic changes of user interests. The system inputs the user's behavior data in time series to train the user's interest in certain types of stores in the current time period. The input user behavior data includes the user's consumption in a certain type of store at a certain point in time. In model training, we also use mean square error as the loss function and use the Adam optimizer, which combines the advantages of momentum and adaptive learning rate to help the model converge quickly and improve accuracy, effectively avoiding overfitting.

[0126] Next, the system uses the quantitative results from the LSTM model to construct a user-store matrix. To optimize personalized recommendations, we also consider the user's spending level and incorporate it into the construction of the user-store matrix. This metric is calculated based on the user's shopping frequency at different stores and the store's price range, providing the system with a method to quantify the user's financial ability. The formula for calculating the user's spending level is:

[0127]

[0128] Among them, T i represents the number of times or frequency of consumption by the user in the i-th store, P i represents the store's price range, and X represents the user's spending level. This approach allows the user's spending level to be weighted in the recommendation algorithm, ensuring personalized and highly relevant recommendations. By incorporating spending level as a weighting factor into the recommendation algorithm, we ensure personalized and highly relevant recommendations.

[0129] Step S40: Using a long short-term memory network model trained with the user behavior time series data to predict the target former user's stores of interest in the current time period, and using a preset collaborative filtering algorithm to calculate and determine a second similarity between the target former user and other users based on the target former user's stores of interest in the current time period, thereby determining the target former user's neighbor users;

[0130] After obtaining user behavior time series data of a target former user, a long short-term memory network model trained with the user behavior time series data is used to predict the target former user's stores of interest in the current time period. A preset collaborative filtering algorithm is used to calculate and determine a second similarity between the target former user and other users based on the target former user's stores of interest in the current time period, thereby determining the target former user's neighboring users.

[0131] Specifically, detailed information about existing users can be collected, including historical purchase history, visit frequency, and preferred store types. This data can be obtained through user behavior analysis and database queries. Model training is performed based on time-series data of user behavior. First, user purchase records for stores are extracted as a training dataset. These records include information about stores visited and purchased by users over different time periods. Using an LSTM model, this time-series data can be trained to quantify the target existing users' interest in various types of stores, effectively capturing the dynamic changes in user interests. After model training is complete, a user-store matrix is ​​constructed based on the quantitative results obtained from model training. The rows of the matrix represent users, the columns represent stores, and each element of the matrix represents the user's rating of the corresponding store.

[0132] To further optimize recommendations, the system also calculates the user's spending level, a metric based on their frequency of purchases at different stores and their price ranges. Ultimately, this level of spending is incorporated into the construction of a user-store matrix for more accurate, personalized recommendations.

[0133] In a further embodiment, historical consumption records and user consumption levels are used as a training set. A long short-term memory network model (LSTM model) trained with this training set is used to quantify the target user's interest in various types of stores, effectively capturing the dynamic changes in user interests. After model training is complete, a user-store matrix is ​​constructed based on the quantitative results obtained from model training. The rows of the matrix represent users, the columns represent stores, and each element of the matrix represents the user's rating of the corresponding store. This is used to predict the stores that the target user is interested in during the current period.

[0134] As shown in Table 1, Table 1 shows the user-store matrix of five users, which includes the users' interest in different store categories and their consumption levels. Through this matrix, the system can understand each user's preference for various stores and the user's own consumption level, so as to predict the stores that the target old users are interested in during the current period.

[0135] Table 1 User-store matrix of five users

[0136]

[0137] In a further embodiment, as shown in Table 2, to further enhance the relevance and accuracy of recommendations, the system also generates a user-user similarity matrix. By comparing the similarities in preferences between different users, it identifies potential groups with similar interests. This similarity matrix is ​​constructed based on users' behavioral preferences for store categories. It helps the system understand which users may have similar shopping interests, thereby providing more personalized recommendations for each user.

[0138] Table 2 Similarity matrix between five users

[0139]

[0140] Table 2 shows the similarity matrix between the five users, where values ​​closer to 1 indicate more similar preferences. By analyzing this matrix, the system can identify groups of users with similar shopping preferences and provide them with more targeted recommendations.

[0141] In some embodiments, a preset collaborative filtering algorithm is used to calculate and determine the second similarity between the target old user and other users based on the stores that the target old user is interested in during the current period. The calculation formula of the second similarity is expressed as:

[0142]

[0143] Among them, sim(u,v) represents the second similarity, u represents the vector of the target old user, and v represents the vector of other users. u and v contain information about the user's interest in the store and consumption level. θ represents the angle between user vectors. The value range of cosine similarity cos(θ) is [0,1]. The larger the value, the more similar the other users are to the target old user.

[0144] The second similarity between the target old user and other users can be calculated and determined by the above calculation formula to determine the neighboring users of the target old user.

[0145] Step S50: predict the estimated ratings of the target old user for the unrated stores based on the ratings of the neighbor users for each store, calculate and determine the second recommendation degree corresponding to each store based on the estimated ratings, the actual distance between the target old user and the store, and the comprehensive recommendation value, and display each store to the user interface of the target old user from high to low according to the second recommendation degree to complete the store recommendation of the shopping mall.

[0146] A preset collaborative filtering algorithm is used to calculate the second similarity between the target old user and other users based on the stores that the target old user is interested in during the current period. After determining the neighboring users of the target old user, the estimated rating value of the target old user for the stores that have not been rated by the target old user is predicted based on the rating values ​​of the neighboring users for each store. The second recommendation degree corresponding to each store is calculated based on the estimated rating value, the actual distance between the target old user and the store, and the comprehensive recommendation value. Each store is displayed to the user interface of the target old user from high to low according to the second recommendation degree to complete the store recommendation of the shopping mall.

[0147] Specifically, calculating the second recommendation score for each store is a key step in ensuring the accuracy and effectiveness of recommendation results. The system assigns appropriate weights to each metric, taking into account the user's interests, the store's actual distance, and the overall recommendation value. Through weighted addition, the final second recommendation score for each store is generated, guiding the system to present the user with the recommendations that best meet their needs. This process considers not only the user's immediate needs but also their long-term preferences, ensuring that the recommendation system can optimize the user experience in an ever-changing environment.

[0148] Throughout the implementation process, the system continuously collects user feedback and behavioral data to adjust its recommendation algorithms and routing strategies in real time. By continuously updating user profiles and store feature models, the system is able to better adapt to changing user needs. Furthermore, the system regularly conducts data analysis and performance evaluations to ensure the effectiveness and accuracy of its recommendations, thereby continuously improving user satisfaction.

[0149] In a specific embodiment, the estimated rating of the target old user for the store that has not been rated is predicted based on the rating of each store by the neighboring users. The calculation formula of the estimated rating is:

[0150]

[0151] in, is the estimated rating value, which represents the predicted rating of the target old user u on the store i, N represents the set of neighboring users with the highest similarity to the target old user u, ω uv represents the similarity between the target old user u and the neighbor user v, r vi represents the rating of store i by neighbor user v;

[0152] The second recommendation corresponding to each store is calculated based on the estimated rating value, the actual distance between the target old user and the store, and the comprehensive recommendation value. The calculation formula for the second recommendation is:

[0153]

[0154] Among them, α, β and γ represent weight coefficients, and R represents the comprehensive recommendation value corresponding to each store.

[0155] Through the above implementation steps, this application achieves efficient route planning and personalized recommendations, providing a flexible and intelligent shopping experience platform. The system can respond to user needs in real time in a rapidly changing shopping environment, significantly improving user satisfaction and providing a viable technical foundation for the future development of smart shopping centers.

[0156] In some embodiments, with the rapid development of the internet and mobile technology, the number of stores in shopping malls continues to increase, and users often feel lost when faced with a vast number of choices. Although traditional recommendation systems can make personalized recommendations by analyzing users' historical behavior data, they still face many challenges, including a lack of sufficient historical interaction data for new users and rapid changes in user preferences. Existing users face difficulties such as incomplete data due to the increasing number of users and the variety of items, as interactions between users and items become increasingly sparse. Therefore, developing an efficient and accurate recommendation method is particularly important.

[0157] In this application, to predict user interest in shopping mall stores, we designed three prediction models based on different recurrent neural network architectures: an RNN model, an LSTM model, and a GRU model. Through comparative experiments, we evaluated the performance of these three models on key performance indicators, including training time, inference time, training loss, validation loss, and accuracy.

[0158] See also Figure 5 and Figure 6 , Figure 5 and Figure 6 Comparisons of training time and inference time for RNN, LSTM, and GRU models are presented. The results show that while the LSTM model takes slightly longer to train than the RNN, its inference time is similar to that of the RNN and outperforms the GRU model. This finding is crucial for real-time recommendation systems that require rapid response to user requests, as the LSTM model can maintain prediction accuracy while meeting the system's stringent real-time requirements.

[0159] See also Figure 7 、 Figure 8 and Figure 9 , Figure 7 、 Figure 8 and Figure 9The performance comparison of the three models in terms of training loss, validation loss, and accuracy is revealed respectively. From the curves of training loss and validation loss, it can be observed that the loss of the LSTM model decreases more steadily during training, and a lower loss value is achieved in the same training cycle. In addition, the accuracy of the LSTM model is also significantly higher than that of the RNN and GRU models, which shows that the LSTM model has significant advantages in capturing long-term dependencies and processing time series data. Taking into account the model's prediction accuracy, training efficiency, and inference efficiency, the LSTM model was selected as the key component in the recommendation system of this application due to its outstanding performance in various indicators. The introduction of the LSTM model not only improves the accuracy of user interest prediction, but also enhances the stability and reliability of the recommendation system when processing complex user behavior data.

[0160] In traditional recommendation systems, distance metrics are typically estimated using Manhattan distance or Euclidean distance. However, these methods do not accurately reflect the actual paths and distances traveled by users. To address this issue, this application proposes an improved Dijkstra algorithm to accurately calculate the actual distances users travel to each store. This algorithm uses a cyclic backtracking method to update the path in real time, providing users with the optimal walking route. This innovation not only improves the accuracy of path planning but also provides an important actual distance metric for recommendation systems.

[0161] In our recommendation strategy for new users, considering their primary challenge is a lack of historical data, the recommendation system needs to comprehensively analyze the user's selected tags and the characteristics of popular stores in the current shopping mall. We innovatively employ a content-based recommendation algorithm that combines multiple metrics to provide diverse recommendations. Specifically, by analyzing the Jaccard similarity coefficient between the user's selected category tags and the store's category tags, the system can effectively alleviate the cold start problem for new users and help them quickly discover stores of potential interest. Furthermore, a weighted formula is used to calculate the final recommendation score, combining the store's comprehensive recommendation value, similarity coefficient, and the actual distance between the user and the store, to ensure that the recommendation results meet user needs to the greatest extent possible.

[0162] For old users, the system uses a collaborative filtering recommendation algorithm that integrates neural networks. The user's historical behavior data is trained through the LSTM model, and the quantitative results are used to form a user-item matrix and calculate the similarity between users. Introducing the user's consumption level into the user-item matrix as a weight factor can more realistically reflect the user's preference for each store. In order to deal with the sparsity problem of user-item interaction, this application adopts a clustering method of subdivided categories. Specifically, by clustering the stores, the store types are converted into unique hot encodings, thereby improving the processability of the data. The clustering process is based on the subdivided categories of the stores, and a large number of store types are summarized into several major categories, each of which is represented by a unique label. This step not only simplifies the data structure, but also facilitates the subsequent prediction of user interests, increases the density of the data, and then uses LSTM for training. This strategy effectively captures the user's interest characteristics and enhances the accuracy of recommendations.

[0163] The system also employs a user similarity rating prediction method to more comprehensively predict existing users' interest in stores. When existing users have no direct ratings or interaction records for certain store types, the system identifies other user groups with high similarity to these users and analyzes the ratings and feedback of these neighboring users on the target store. By calculating the similarity weights between the target user and their neighboring users, the system can reasonably predict the target user's potential ratings for unrated stores. This prediction method based on neighboring user ratings not only supplements the missing data in the user-item matrix but also improves the accuracy and diversity of recommendation results. In implementing this method, the system first identifies neighboring user groups with similar consumption habits and preferences to the target user. It then collects the ratings and feedback of these neighboring users on the target store. It then calculates a weighted average based on the similarity between the target user and the neighboring users to derive the target user's predicted rating for the store. This predicted rating is then applied to the recommendation algorithm to dynamically update the target user's recommendation list, ensuring the relevance and novelty of the recommendations. By combining the user-item matrix and user similarity score prediction, the system can provide richer and more personalized store recommendations for existing users, ensuring the accuracy and timeliness of the recommendation results even when user-item interaction data is sparse.

[0164] In the core part of the recommendation algorithm, this application focuses on comprehensively considering user behavior data, store characteristics and real-time data to generate accurate personalized recommendations. The system builds a comprehensive multi-indicator data set to collect the comprehensive score, number of reviews and the number of new reviews in the past month of each store, and constructs a multi-dimensional feature model of the store. During the data processing process, the logarithmic scaling method is used to process the long-tail distribution of the number of reviews, and the score and number of reviews are comprehensively considered to calculate the popularity value of each store. In addition, the popularity value is calculated using a weighted average model, which comprehensively considers the comprehensive score, number of reviews and number of new reviews, and sets different weights according to the importance of each indicator, and finally generates a comprehensive recommendation value for each store, providing a solid foundation for subsequent recommendation algorithms.

[0165] When processing user data, the system records the price ranges users enter stores within and quantifies them into a number from 1 to 4, representing a tiered price range from low to high. By counting the frequency of user visits to each store and combining price ranges and number of visits, the system can more accurately estimate a user's spending level and generate a user profile, laying the foundation for personalized recommendations. Furthermore, the system constructs a user-user matrix and calculates similarities between different users, further enhancing the performance of the recommendation system. This similarity calculation helps the system better understand the preference distribution of user groups, thereby providing more personalized recommendations for each user.

[0166] This application validates the effectiveness of the proposed method through experiments, including training and evaluating a neural network model, validating the recommendation algorithm, and testing the path planning algorithm. The experimental results demonstrate that the LSTM model effectively captures user interests and the recommendation algorithm effectively discovers users' hidden preferences, demonstrating the superiority of this application's recommendation system in terms of accuracy, real-time performance, and user satisfaction.

[0167] As can be seen from the above embodiments, compared with the prior art, the present application addresses the following issues: the interaction between new users and items is very sparse, which makes it more difficult to capture the user's true preferences. For old users, as the number of users and the variety of items increases, the interaction between users and items becomes increasingly sparse, making it difficult for the system to accurately capture the user's true preferences. The present application includes but is not limited to the following beneficial effects:

[0168] First, by using an improved Dijkstra algorithm, the system can accurately calculate the actual walking distance of users to each store, taking into account the user's actual walking path rather than just traditional distance metrics (such as Manhattan distance or Euclidean distance). This precise path planning greatly improves the user's navigation experience in the shopping mall, provides users with the optimal walking route, and further optimizes the shopping process;

[0169] Secondly, this application solves the cold start problem for new users. By combining user-selected tags with the characteristics of popular stores in the current shopping mall, the system can effectively alleviate the cold start problem. The Jaccard similarity coefficient is used to analyze the relationship between user-selected category tags and store category tags, allowing new users to quickly discover stores that match their potential interests.

[0170] Third, for existing users, the system uses an LSTM neural network model to analyze historical user behavior data, accurately capturing their interests. Combined with a clustering method based on segmented categories, this reduces sparsity issues, improving recommendation accuracy and data processability. By weighting the factors in the user-item matrix, the system can more accurately reflect user preferences for each store, enhancing the level of personalized recommendations.

[0171] Fourthly, this application utilizes a user similarity rating prediction method to more comprehensively predict a user's interest in a store. When a user lacks direct interaction history with a store, the system can use the ratings and feedback of neighboring users to predict the target user's potential preferences. This weighted prediction method based on neighboring users effectively fills gaps in the user-item matrix, further improving the accuracy and diversity of recommendations.

[0172] Fifth, at the core of our recommendation algorithm, the system comprehensively considers data from multiple dimensions (such as store ratings, number of reviews, and newly added reviews) and calculates a store's popularity and buzz using a weighted average model. This multi-dimensional, comprehensive calculation ensures the accuracy and timeliness of our recommendations, ensuring they better align with user interests and needs.

[0173] Sixth, by analyzing data such as the price range and frequency of visits to stores, the system can more accurately build user profiles, analyze their spending levels, and provide a more reliable basis for personalized recommendations. Computing similarities between users further enhances the system's recommendation capabilities, ensuring that each user receives recommendations that best meet their needs.

[0174] Seventh, this application improves user satisfaction and shopping experience. Combining precise route planning, real-time updates, and personalized recommendations, the system can significantly enhance the user shopping experience, reduce unnecessary time waste, and help users quickly discover stores that match their interests. Overall, the recommendation system can significantly increase user satisfaction, boosting shopping mall traffic and user retention.

[0175] In summary, this application implements a recommendation system that comprehensively utilizes user behavior data, store characteristics, and actual distance. By combining deep learning models with multiple algorithms, it provides users with personalized store recommendations, significantly improving the user experience and increasing customer satisfaction. This technical solution not only has good practicality, but also contributes to the construction of future smart shopping navigation systems, adding more convenience and fun to the user's shopping experience. Combining positioning technology, real-time data analysis, and personalized recommendation algorithms, this application demonstrates broad application prospects and can provide strong support for future smart retail and shopping center management.

[0176] See also Figure 10A shopping mall store recommendation device provided to meet one of the purposes of this application includes a comprehensive recommendation value determination module 1100, a new user store recommendation module 1200, a user behavior data acquisition module 1300, a neighbor user determination module 1400 and an old user store recommendation module 1500. Among them, the comprehensive recommendation value determination module 1100 is configured to obtain the number of store reviews and store comprehensive scores corresponding to each store in the shopping mall, determine the store popularity value corresponding to the store based on the number of store reviews and the store comprehensive score, determine the store popularity corresponding to the store based on the store popularity value, the store comprehensive score and the number of new reviews of the store within a preset time period, and determine the comprehensive recommendation value corresponding to each store based on the store popularity value and the store popularity; the new user store recommendation module 1200 is configured to respond to the instruction to recommend a store to the target new user, obtain the user interest category label of the target new user and the store category label corresponding to each store, calculate and determine the first similarity between the user interest category label and the store category label, determine the first recommendation degree corresponding to each store based on the comprehensive recommendation value, the first similarity and the actual distance between the target new user and the store, and display each store in the user interface of the target new user from high to low according to the first recommendation degree; the user behavior data acquisition module 1300 is configured to respond to the instruction to recommend a store to the old user, obtain the user interest category label of the target old user, and calculate and determine the first similarity between the user interest category label and the store category label, determine the first recommendation degree corresponding to each store based on the comprehensive recommendation value, the first similarity and the actual distance between the target new user and the store, and display each store in the user interface of the target new user from high to low according to the first recommendation degree; User behavior time series data, wherein the user behavior time series data includes historical consumption records and user consumption levels, the historical consumption records represent information about stores visited and consumed by users in different time periods, and the user consumption levels are obtained by the user's consumption frequency in different stores and the price range of the stores; a neighbor user determination module 1400 is configured to use a long short-term memory network model trained with the user behavior time series data to predict the stores of interest to the target old user in the current time period, and use a preset collaborative filtering algorithm to calculate and determine the second similarity between the target old user and other users based on the stores of interest to the target old user in the current time period to determine the neighbor users of the target old user; an old user store recommendation module 1500 is configured to predict the target old user's estimated rating value for the unrated stores based on the rating values ​​of the neighbor users for each store, calculate and determine the second recommendation degree corresponding to each store based on the estimated rating value, the actual distance between the target old user and the store, and the comprehensive recommendation value, and display each store in the user interface of the target old user from high to low according to the second recommendation degree to complete the store recommendation of the shopping mall.

[0177] Based on any embodiment of this application, please refer to Figure 11 Another embodiment of the present application further provides an electronic device, which can be implemented by a computer device, such as Figure 11 As shown, a schematic diagram of the internal structure of a computer device. The computer device includes a processor, a computer-readable storage medium, a memory, and a network interface connected via a system bus. The computer-readable storage medium of the computer device stores an operating system, a database, and computer-readable instructions. The database may store a control information sequence. When the computer-readable instructions are executed by the processor, the processor may implement a shopping mall store recommendation method. The processor of the computer device is used to provide computing and control capabilities to support the operation of the entire computer device. The memory of the computer device may store computer-readable instructions. When the computer-readable instructions are executed by the processor, the processor may execute the shopping mall store recommendation method of the present application. The network interface of the computer device is used to connect and communicate with a terminal. Those skilled in the art will understand that Figure 11 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than shown in the figure, or combine certain components, or have a different component arrangement.

[0178] In this embodiment, the processor is used to execute Figure 10 The memory stores the program code and various data required to execute the modules and submodules. The network interface is used to transmit data between user terminals and servers. The memory in this embodiment stores the program code and data required to execute all modules and submodules in the shopping mall store recommendation device of this application. The server can call the server's program code and data to execute the functions of all submodules.

[0179] The present application also provides a storage medium storing computer-readable instructions. When the computer-readable instructions are executed by one or more processors, the one or more processors execute the steps of the shopping mall store recommendation method described in any embodiment of the present application.

[0180] The present application also provides a computer program product, including a computer program / instruction, which, when executed by one or more processors, implements the steps of the shopping mall store recommendation method described in any embodiment of the present application.

[0181] Those skilled in the art will appreciate that all or part of the processes in the above-mentioned embodiments of the present application can be implemented by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium. When the program is executed, it can include the processes of the above-mentioned embodiments of the method. The aforementioned storage medium can be a computer-readable storage medium such as a magnetic disk, an optical disk, a read-only memory (ROM), or a random access memory (RAM).

[0182] The above description is only part of the implementation methods of the present application. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present application. These improvements and modifications should also be regarded as the scope of protection of the present application.

[0183] In summary, this application implements a recommendation system that comprehensively utilizes user behavior data, store characteristics, and actual distance. By combining deep learning models with multiple algorithms, it provides users with personalized store recommendations, significantly improving the user experience and increasing customer satisfaction. This technical solution not only has good practicality, but also contributes to the construction of future smart shopping navigation systems, adding more convenience and fun to the user's shopping experience. Combining positioning technology, real-time data analysis, and personalized recommendation algorithms, this application demonstrates broad application prospects and can provide strong support for future smart retail and shopping center management.

Claims

1. A method for recommending stores in a shopping mall, characterized in that: include: Obtain the number of store reviews and the store comprehensive score corresponding to each store in the shopping mall, determine the store popularity value corresponding to the store based on the number of store reviews and the store comprehensive score, determine the store popularity corresponding to the store based on the store popularity value, the store comprehensive score and the number of new reviews of the store within a preset time period, and determine the comprehensive recommendation value corresponding to each store based on the store popularity value and the store popularity. The calculation formula for the store popularity value is: Among them, Hotness represents the store popularity value corresponding to each store, R1 represents the number of store reviews received by the store, and r represents the store's comprehensive score; The calculation formula for the store popularity is: P=α·Hotness+β·S+γ·ΔN, Where P represents the store popularity of each store, S represents the store's comprehensive score, ΔN represents the number of new reviews of the store within a preset time range, and α, β, and γ represent weight coefficients; The calculation formula of the comprehensive recommended value is: R=λ·P+(1-λ)·Hotness, Among them, R is the comprehensive recommendation value corresponding to each store, and λ is the weight coefficient, which is used to balance the importance of store popularity and store heat value; In response to an instruction to recommend a store to a target new user, obtaining a user interest category tag of the target new user and a store category tag corresponding to each store, calculating and determining a first similarity between the user interest category tag and the store category tag, determining a first recommendation degree corresponding to each store based on the comprehensive recommendation value, the first similarity, and the actual distance between the target new user and the store, and displaying each store on a user interface of the target new user in descending order of the first recommendation degree; In response to an instruction to recommend a store to an old user, obtain user behavior time series data of the target old user, wherein the user behavior time series data includes historical consumption records and user consumption levels. The historical consumption records represent information about stores visited and consumed by the user in different time periods. The user consumption level is obtained by the user's consumption frequency at different stores and the store's price range; A long short-term memory network model trained with the user behavior time series data is used to predict the target former user's stores of interest in the current time period, and a preset collaborative filtering algorithm is used to calculate and determine a second similarity between the target former user and other users based on the target former user's stores of interest in the current time period, so as to determine the target former user's neighboring users; Based on the ratings of the neighbor users on each store, the estimated ratings of the target old user on the stores that have not been rated by the target old user are predicted. The second recommendation degree corresponding to each store is calculated and determined based on the estimated ratings, the actual distance between the target old user and the store, and the comprehensive recommendation value. Each store is displayed to the user interface of the target old user from high to low according to the second recommendation degree to complete the store recommendation of the shopping mall.

2. The shopping mall store recommendation method according to claim 1, characterized in that: The calculation formula for the user consumption level is: Among them, Ti represents the number or frequency of consumption by the user in the i-th store, Pi represents the price range of the store, and X represents the user's consumption level.

3. The shopping mall store recommendation method according to claim 1, characterized in that: A preset collaborative filtering algorithm is used to calculate and determine the second similarity between the target old user and other users based on the stores that the target old user is interested in during the current period. The calculation formula of the second similarity is expressed as: Among them, sim(u,v) represents the second similarity, u represents the vector of the target old user, and v represents the vector of other users. u and v contain information about the user's interest in the store and consumption level. θ represents the angle between user vectors. The value range of cosine similarity cos(θ) is [0,1]. The larger the value, the more similar the other users are to the target old user.

4. The shopping mall store recommendation method according to claim 1, characterized in that: The steps for determining the actual distance between the user and the store include: Obtain a shopping mall map to determine the starting location of target new or existing users and the locations of multiple target stores; An improved Dijkstra algorithm is used to perform path planning based on the starting location and multiple target store locations to determine the actual distance that the target new user or the target old user needs to travel from the current location to each target store.

5. A shopping mall store recommendation device, characterized in that: include: The comprehensive recommendation value determination module is configured to obtain the number of store reviews and the store comprehensive score corresponding to each store in the shopping mall, determine the store popularity value corresponding to the store based on the number of store reviews and the store comprehensive score, determine the store popularity corresponding to the store based on the store popularity value, the store comprehensive score and the number of new reviews of the store within a preset time period, and determine the comprehensive recommendation value corresponding to each store based on the store popularity value and the store popularity, wherein the calculation formula of the store popularity value is: Among them, Hotness represents the store popularity value corresponding to each store, R1 represents the number of store reviews received by the store, and r represents the store's comprehensive score; The calculation formula for the store popularity is: P=α·Hotness+β·S+γ·ΔN, Where P represents the store popularity of each store, S represents the store's comprehensive score, ΔN represents the number of new reviews of the store within a preset time range, and α, β, and γ represent weight coefficients; The calculation formula of the comprehensive recommended value is: R=λ·P+(1-λ)·Hotness, Among them, R is the comprehensive recommendation value corresponding to each store, and λ is the weight coefficient, which is used to balance the importance of store popularity and store heat value; A new user store recommendation module is configured to respond to an instruction to recommend a store to a target new user, obtain a user interest category tag of the target new user and a store category tag corresponding to each store, calculate and determine a first similarity between the user interest category tag and the store category tag, determine a first recommendation degree corresponding to each store based on the comprehensive recommendation value, the first similarity, and the actual distance between the target new user and the store, and display each store on the user interface of the target new user in descending order of the first recommendation degree; A user behavior data acquisition module is configured to respond to an instruction to recommend stores to an old user and acquire user behavior time series data of a target old user, wherein the user behavior time series data includes historical consumption records and user consumption levels. The historical consumption records represent information about stores visited and consumed by the user in different time periods. The user consumption level is obtained by the user's consumption frequency at different stores and the store's price range; a neighbor user determination module configured to use a long short-term memory network model trained using the user behavior time series data to predict the target former user's stores of interest in the current time period, and to use a preset collaborative filtering algorithm to calculate and determine a second similarity between the target former user and other users based on the target former user's stores of interest in the current time period, so as to determine the target former user's neighbor users; The old user store recommendation module is configured to predict the estimated rating value of the target old user for the unrated stores based on the rating values ​​of the neighbor users for each store, calculate and determine the second recommendation degree corresponding to each store based on the estimated rating value, the actual distance between the target old user and the store, and the comprehensive recommendation value, and display each store to the user interface of the target old user from high to low according to the second recommendation degree to complete the store recommendation of the shopping mall.

6. An electronic device comprising a central processing unit and a memory, characterized in that: The central processing unit is configured to call and run a computer program stored in the memory to execute the steps of the method according to any one of claims 1 to 4.

7. A computer-readable storage medium, characterized in that It stores a computer program implemented according to the method described in any one of claims 1 to 4 in the form of computer-readable instructions, and when the computer program is called and executed by a computer, the steps included in the corresponding method are executed.

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