Tourist attraction recommendation method and system based on big data, equipment and storage medium

Abstract

The invention provides a tourist attraction recommendation method and system based on big data, equipment and a storage medium, and belongs to the technical field of data processing, and the method comprises the steps: determining a first user matched with a target user on an intelligent recommendation platform based on the personal information of the target user; generating a first recommendation scheme for the target user based on the historical behavior information of the target user and the historical behavior information of the first user; optimizing a first recommendation scheme of the target user based on the spatio-temporal information to obtain a target recommendation scheme; wherein the spatio-temporal information comprises time information and spatial information. The accuracy of the recommendation scheme can be improved, and the user experience is improved.

Description

Technical Field

[0001] The present disclosure belongs to the field of data processing technology, and more specifically, to a tourist attraction recommendation method and system, device, and storage medium based on big data. Background Art

[0002] With the rapid development of the tourism industry and the diversification of tourist needs, traditional tourism planning methods have been unable to meet the personalized needs of modern tourists. Traditional tourism planning often relies on manually formulated travel routes and scenic spot recommendations, lacks accurate recommendations for tourists' personalized needs, and has a poor user experience. Summary of the invention

[0003] The purpose of the present invention is to provide a tourist attraction recommendation method and system, device, and storage medium based on big data to improve the accuracy of recommendation solutions and enhance user experience.

[0004] In a first aspect of the embodiments of the present disclosure, a tourist attraction recommendation method based on big data is provided, which is applied to an intelligent recommendation platform, including: Determining a first user on the intelligent recommendation platform that matches the target user based on the target user's personal information; generating a first recommendation scheme for the target user based on the historical behavior information of the target user and the historical behavior information of the first user; The first recommendation scheme for the target user is optimized based on the spatiotemporal information to obtain a target recommendation scheme; wherein the spatiotemporal information includes time information and space information.

[0005] A second aspect of the disclosed embodiment provides a tourist attraction recommendation system based on big data, which is applied to an intelligent recommendation platform and includes: A matching module, configured to determine a first user on the intelligent recommendation platform that matches the target user based on the target user's personal information; A first determination module, configured to generate a first recommendation scheme for the target user based on the historical behavior information of the target user and the historical behavior information of the first user; The solution recommendation module is used to optimize the first recommended solution of the target user based on the spatiotemporal information to obtain a target recommended solution; wherein the spatiotemporal information includes time information and space information.

[0006] According to a third aspect of an embodiment of the present disclosure, an electronic device is provided, including a memory, a processor, and a computer program stored in the memory and running on the processor, wherein the processor implements the steps of the above-mentioned method for recommending tourist attractions based on big data when executing the computer program.

[0007] According to a fourth aspect of an embodiment of the present disclosure, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the above-mentioned tourist attraction recommendation method based on big data are implemented.

[0008] The beneficial effects of the tourist attraction recommendation method, system, device, and storage medium based on big data provided by the embodiments of the present disclosure are: On the one hand, the present disclosure fully utilizes the user data resources on the intelligent recommendation platform by accurately matching the target user with similar users (first users). Based on the comprehensive analysis of the personal information and historical behavior information of the target user and the first user, a first recommendation scheme that is closer to the actual needs and preferences of the target user can be generated. This personalized recommendation method not only improves the accuracy of the recommendation, but also enhances the satisfaction of the user experience, allowing users to more easily find a travel plan that suits them.

[0009] On the other hand, the present disclosure further introduces spatiotemporal information to optimize the first recommendation scheme, thereby obtaining a more practical and reasonable target recommendation scheme. The consideration of spatiotemporal information enables the recommendation scheme to better adapt to the travel time and location of the target user, avoiding unreasonable itinerary arrangements caused by time conflicts or inconvenient geographical locations. This optimization not only improves the practicality of the recommendation scheme, but also improves the travel efficiency and tourism experience quality of the user. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] In order to more clearly illustrate the technical solutions in the embodiments of the present disclosure, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present disclosure. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0011] Figure 1 A flowchart of a method for recommending tourist attractions based on big data provided by an embodiment of the present disclosure; Figure 2 A structural block diagram of a tourist attraction recommendation system based on big data provided by an embodiment of the present disclosure; Figure 3 A schematic block diagram of an electronic device provided by an embodiment of the present disclosure. DETAILED DESCRIPTION

[0012] In the following description, specific details such as specific system structures and technologies are provided for the purpose of illustration rather than limitation, so as to provide a thorough understanding of the embodiments of the present disclosure. However, it should be clear to those skilled in the art that the present disclosure may be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted to avoid obstructing the description of the present disclosure with unnecessary details.

[0013] In order to make the purpose, technical solutions and advantages of the present disclosure more clear, specific embodiments will be described below in conjunction with the accompanying drawings.

[0014] Please refer to Figure 1 , Figure 1 A flowchart of a method for recommending tourist attractions based on big data provided by an embodiment of the present disclosure is provided. The method is applied to an intelligent recommendation platform and includes: S101: Determine a first user matching the target user on the intelligent recommendation platform based on the target user's personal information.

[0015] In this embodiment, the intelligent recommendation platform is a software system that uses artificial intelligence algorithms and big data technology. It can collect, store and analyze various user data, including but not limited to user basic information, behavior data (such as browsing history, purchase behavior, etc.) and feedback data. Then, based on these data, it provides users with personalized travel planning recommendation services.

[0016] The target user is a specific individual that the intelligent recommendation platform targets for recommendation operations. This individual has his or her own unique personal information, such as age, gender, hobbies, consumption habits, geographic location, etc. The platform collects and analyzes the target user's information in order to better understand their needs and preferences, so as to find other users or recommended content that match them.

[0017] The first user is other users who have a high degree of match with the target user on the intelligent recommendation platform. This matching is usually based on a series of matching rules and algorithms set by the platform. These rules involve multiple dimensions of the user's personal information, such as the similarity of interests and hobbies, the consistency of consumption behavior, the relevance of social circles, etc.

[0018] Determining the first user matching the target user on the intelligent recommendation platform based on the target user's personal information includes: Build a user profile for each user based on the user information on the intelligent recommendation platform, and calculate the similarity between the target user profile and the platform user profile based on a preset algorithm; The first user is determined based on the similarity between the target user portrait and the user portrait of the platform user.

[0019] In this embodiment, a user portrait is constructed for each user based on the user information on the intelligent recommendation platform. The user portrait contains a variety of user information, such as basic attributes (age, gender, geographic location, etc.), interests and hobbies (favorite movie types, music styles, sports, etc.), and consumption behaviors (purchase frequency, purchase product categories, consumption amounts, etc.). The similarity between the target user portrait and other user portraits is calculated by algorithms such as cosine similarity, Euclidean distance, and Pearson correlation coefficient, and the user with the highest similarity is taken as the first user. For example, on the intelligent recommendation platform, if the target user and another user have many common tags in the dimension of interests and hobbies (such as both like science fiction novels and historical documentaries), then their similarity in this dimension will be relatively high.

[0020] Determining the first user matching the target user on the intelligent recommendation platform based on the target user's personal information also includes: The social relationship of the user is determined based on the information of the user on the intelligent recommendation platform, and the first user matching the target user on the intelligent recommendation platform is determined based on the social relationship.

[0021] In this embodiment, the social relationship is determined based on the friend list information and / or the follow list information.

[0022] Determine the user's social relationship based on the user's information on the smart recommendation platform. For example, the smart recommendation platform usually records the user's friend list information. In addition to this explicitly added friend relationship, potential friend relationship tendencies can also be inferred through some interactive behaviors. For example, on the platform, if user C often likes and comments on user D's travel notes, and user D also has similar interactions with user C's related content, although they may not have been officially added as friends, it can be considered that there is a potential friend relationship between them based on learning, communication and interaction.

[0023] When matching the first user based on the target user's friend list, determine other users who have a first number of common friends with the target user, and determine the user as the first user. The first number is proportional to the total number of friends of the target user. For example, the total number of friends of the target user is S, and the first number is kS, k∈(0.2, 1). For example, if the target user E has friends F, G, H, etc., the platform will analyze the friend lists of other users and find that user I also has friends F and G, then user I has more common friends with the target user E.

[0024] S102: Generate a first recommendation scheme for the target user based on the historical behavior information of the target user and the historical behavior information of the first user.

[0025] In this embodiment, historical behavior information refers to the user's travel-related behavior and activity records in the past period of time, including but not limited to the selection of tourist destinations, accommodation reservations, transportation mode selection, scenic spot tours, dining consumption, shopping records, etc. The recommendation plan is a suggestion or plan generated based on the user's historical behavior information and algorithm analysis, aiming to meet the user's travel needs. The recommendation plan may include recommended tourist destinations, accommodation selections, activity arrangements, transportation modes, etc. In this embodiment, the historical behavior information of the target user and the historical behavior information of the first user may include travel records, travel activities, accommodation preferences and other information. The travel record includes which tourist destinations the target user has visited in the past, such as popular tourist cities such as Beijing, Shanghai, Paris, etc., or areas where niche special attractions are located. The time span of travel is also critical, whether you like to travel during holidays, or use the annual leave to choose off-peak travel. For example, some users always choose to travel during long holidays such as National Day and Spring Festival, indicating that he may be more inclined to enjoy the tourist atmosphere when everyone has free time; while some users will travel during non-holiday periods in May or October each year, perhaps to avoid peak traffic and enjoy a quieter travel experience.

[0026] What activities have been participated in at the tourist destination can also reflect their preferences. For example, when traveling at the seaside, do you like to simply sunbathe and play in the water on the beach, or are you keen on participating in water sports such as surfing and diving? When traveling in the mountains, are you willing to hike up the mountain to enjoy the natural scenery, or will you choose to take a cable car or other convenient ways to visit. Accommodation preferences: that is, do you like to stay in star-rated hotels to enjoy comfortable services, or are you more inclined to choose characteristic homestays to experience local customs. For example, some users choose to stay in local characteristic homestays every time they travel, probably because they value that homestays can provide an experience closer to local life, and they can communicate with homestay owners and other guests and have a deep understanding of local culture; while some users will choose star-rated hotels in big cities to ensure service quality and convenience according to the situation of the tourist destination, and choose homestays in some places with beautiful scenery but relatively simple supporting facilities.

[0027] In this embodiment, a first recommendation scheme for the target user is generated based on the historical behavior information of the target user and the historical behavior information of the first user: the historical behavior information of the target user and the first user is collected, and the collected data is cleaned to remove invalid or abnormal data. The explicit preferences of the target user and the first user for tourist destinations, activities, accommodation, etc., such as high scores, positive comments, etc., are analyzed. The implicit feedback data such as the browsing behavior, stay time, and click-through rate of the target user and the first user are analyzed to mine their potential tourist preferences. Similarity calculation methods such as cosine similarity and Pearson correlation can be used to calculate the similarity between the target user and the first user based on the historical behavior information of the user. Based on the calculated similarity, the first user set that is most similar to the target user is screened. Based on the screened similar user set, the tourist destinations, activities, accommodation, etc. that these users like in common are analyzed as candidate sets recommended to the target user. Combined with the historical behavior information and preferences of the target user, the candidate set is further screened and optimized to generate a personalized recommendation scheme for the target user.

[0028] S103: Optimizing the first recommendation scheme for the target user based on the spatiotemporal information to obtain a target recommendation scheme; wherein the spatiotemporal information includes time information and space information.

[0029] In this embodiment, the spatiotemporal information includes time information and space information. In travel recommendations, the spatiotemporal information may include the user's travel time (such as season, holidays), travel locations (such as cities, attractions), and spatial relationships between locations (such as distance, transportation methods), etc.

[0030] The first recommendation is a preliminary recommendation based on the user's historical behavior information and other relevant data (such as user preferences, budget, etc.). This plan contains a series of recommendations for travel destinations, activities, accommodation, etc. The target recommendation is the final recommendation after optimization. This plan not only takes into account the historical behavior information and preferences of the target user, but also combines spatiotemporal information to provide more accurate and practical travel suggestions. The target recommendation is usually more in line with the user's expectations and needs, which helps to improve user satisfaction and loyalty.

[0031] In this embodiment, the target user's travel plan time, travel location, itinerary and other spatiotemporal data are collected. The target user's travel plan time is analyzed, and the impact of different seasons and holidays on travel choices is considered. The activity arrangement, scenic spot tour order, etc. in the recommended plan are adjusted according to the time factor. The geographical location information, scenic spot distribution, traffic conditions, etc. related to the tourist location are obtained. The geographical location relationship of the tourist location is analyzed, and the distance and transportation mode between the scenic spots are calculated. Considering the travel mode and preferences of the target user, the itinerary route and transportation arrangement are optimized. The target user's travel plan time, travel location, itinerary and other spatiotemporal data can be information that the target user enters into the intelligent recommendation platform by himself. The intelligent recommendation platform inputs the information entered by the user and the first recommendation plan into the data analysis model (convolutional neural network model) to obtain the target recommendation plan. For example, the first recommendation plan is to travel to Beijing during October 1st, go to place a on the first day, go to place b on the second day, and go to place c on the third day, and the means of transportation is the subway. However, the target user enters information on the platform and plans to travel to a place with few people and a place with an open field of vision on October 1st. The intelligent recommendation platform can judge based on the input information of the target user that there are many people traveling to Beijing during October 1st, the population density is high, and the field of vision is not open enough. Therefore, the first recommendation plan is changed to traveling to Ulanqab during October 1st, going to aa on the first day, bb on the second day, and cc on the third day. Considering the long distance between the various locations, long-distance buses are selected as the means of transportation.

[0032] Based on the results of spatiotemporal analysis, filter out travel destinations and activities that meet the needs and preferences of target users.

[0033] The selected recommended contents are sorted, and scenic spots and activities with close distance, suitable time and high evaluation are recommended first. The recommended scheme is adjusted individually according to the personal preferences and special needs of the target users. For example, local specialty restaurants are recommended according to the user's dietary preferences, and suitable entertainment activities are recommended according to the user's leisure needs. The platform obtains and integrates real-time information such as weather conditions and the flow of people in scenic spots by connecting to the weather forecast platform and the scenic spot online monitoring system. The recommended scheme is dynamically adjusted according to the real-time information to ensure the accuracy and practicality of the recommended content. For example, when the platform detects that there will be heavy rainfall in the recommended place d during the travel plan time of the target user, the destination in the recommended scheme is modified. If the platform detects that the flow of people in the target scenic spot in the recommended place d during the travel plan time is full, the target scenic spot in the recommended scheme is modified. The optimized recommended scheme is comprehensively evaluated to ensure that it meets the needs of the target users in terms of time, space, user preferences, etc. The comprehensive evaluation method can be: matching the destination, scenic spot, route, transportation, etc. in the recommended scheme with the user's needs, setting the corresponding matching score, and if the score is greater than the preset score (platform setting), the user's needs are met. For example, if the score of matching between the destination and the user's needs is 70, the score of matching between the scenic spot and the user's needs is 90, the score of matching between the route and the user's needs is 80, the score of matching between transportation and the user's needs is 90, and the average score is 82.5 (greater than the preset score of 80), then the recommended plan meets the needs of the target users.

[0034] Based on the evaluation results, generate the final target recommendation plan. The plan should include detailed itinerary arrangements, scenic spot introductions, transportation methods, accommodation suggestions, etc. In the recommendation, balance popular scenic spots (users may be interested in but have not visited yet) and special niche scenic spots (in line with the user's unique preferences) to increase the diversity of recommendations. And use the map application program interface to generate the optimal tour route based on the recommended scenic spot list, taking into account time efficiency, transportation convenience and the logical order between scenic spots (such as avoiding repeated routes and sorting by points of interest).

[0035] It can be concluded from the above that, on the one hand, the present disclosure fully utilizes the user data resources on the intelligent recommendation platform by accurately matching the target user with similar users (first users). Based on the comprehensive analysis of the personal information and historical behavior information of the target user and the first user, a first recommendation scheme that is closer to the actual needs and preferences of the target user can be generated. This personalized recommendation method not only improves the accuracy of the recommendation, but also enhances the satisfaction of the user experience, allowing users to more easily find a travel plan that suits them.

[0036] On the other hand, the present disclosure further introduces spatiotemporal information to optimize the first recommendation scheme, thereby obtaining a more practical and reasonable target recommendation scheme. The consideration of spatiotemporal information enables the recommendation scheme to better adapt to the travel time and location of the target user, avoiding unreasonable itinerary arrangements caused by time conflicts or inconvenient geographical locations. This optimization not only improves the practicality of the recommendation scheme, but also improves the travel efficiency and tourism experience quality of the user.

[0037] In one embodiment of the present disclosure, optimizing the first recommendation scheme of the target user based on the spatiotemporal information to obtain the target recommendation scheme includes: Based on the spatiotemporal information and the input information of the target user on the intelligent recommendation platform, the first recommendation plan for the target user is optimized to obtain the target recommendation plan.

[0038] The input information includes text information or voice information; If the input information is text information, the first recommendation scheme for the target user is optimized based on the spatiotemporal information and the text information to obtain a target recommendation scheme; If the input information is voice information, the voice features are determined based on the voice information, and the first recommendation scheme for the target user is optimized based on the spatiotemporal information and the voice features to obtain a target recommendation scheme.

[0039] In this embodiment, input information refers to additional information provided by the target user on the intelligent recommendation platform, which is used to further optimize the recommendation scheme. This information can be text information (such as specific needs and preferences input by the user) or voice information (such as needs and questions input by the user through voice).

[0040] The input information is different from the target user's personal information and the target user's historical behavior information. The input information is the information that the target user enters into the smart recommendation platform when he / she has travel needs; the user's personal information is the information filled in when the user registers the smart recommendation platform; the target user's historical behavior information is the information that the smart recommendation platform obtains from other associated APPs of the target user, such as Taobao, 12306, Tongcheng Travel, Xiaohongshu, etc. The smart recommendation platform can obtain information from other associated APPs of the target user in the following ways: user authorization, cross-application ID technology, etc. For example, when the target user uses the smart recommendation platform, the platform will request certain permissions of the user, such as reading the address book, accessing location information, reading the application list, etc. Once the target user clicks the authorization button, it means that he / she agrees that the platform will obtain relevant information about him / her. Cross-application ID technology is a technology used to identify the same user between different APPs. Through this technology, the smart recommendation platform can track the behavior trajectory of the target user on different APPs, thereby collecting more comprehensive historical behavior information. However, the use of cross-application ID technology must comply with relevant laws and regulations and privacy policies to ensure the legality and security of user data.

[0041] Text information refers to the information that users enter on the intelligent recommendation platform through a keyboard or other text input device. This information usually includes the user's specific needs, preferences, constraints, etc. Voice information refers to the information that users enter on the intelligent recommendation platform through a voice input device (such as a microphone). This information needs to be recognized and processed by voice to extract the user's intentions and needs. Voice features refer to the distinguishing features extracted from voice information, such as language type, speaking speed, intonation, timbre, etc. These features can help the system understand the user's voice input more accurately and be used to optimize the recommendation plan.

[0042] The target recommendation is the final optimized travel planning suggestion. This plan not only takes into account the personal information and historical behavior information of the target user, but also combines the temporal and spatial information and the real-time input information of the user on the intelligent recommendation platform (whether text information or voice information). The target recommendation plan is usually more in line with the user's expectations and needs, which helps to improve user satisfaction and loyalty.

[0043] Determining speech features based on speech information includes: recognizing the speech information to obtain first audio information, and extracting features from the first audio information to obtain speech features. Speech features include basic physical features, linguistic features, and speech features. Basic physical features include pitch, volume, timbre, etc. Linguistic features include phonemes, syllables, intonation, rhythm, and continuity, etc. Speech features include pronunciation habits, emotions, etc.

[0044] The first recommendation scheme for the target user is optimized based on the spatiotemporal information and the voice features to obtain the target recommendation scheme, including: Acquire the first location, travel time and voice characteristics of the target user; determine the weather characteristics, traffic characteristics and scenic spot information of the second location based on the first location and travel time of the target user; the second location is the location of the travel destination; Based on the weather characteristics, traffic characteristics, scenic spot information and voice characteristics of the second location, the voice guide, the sequence of tourist attractions and the mode of transportation in the first recommendation scheme are optimized to obtain a target recommendation scheme.

[0045] In this embodiment, the first location is the current location of the target user. The first location and travel time of the target user are determined based on the spatiotemporal information, and the voice guide in the first recommendation scheme is determined based on the voice features. The voice features also include the language preference and accent features of the target user. The voice guide in the first recommendation scheme is determined based on the voice features, including: determining the broadcast language for the voice guide based on the language preference of the target user; and adjusting the speech speed and tone of the voice guide based on the accent features of the target user.

[0046] For example, the smart travel recommendation platform receives the travel planning needs of the target user (Mr. Zhang), who plans to travel to Beijing in the next three days and hopes to obtain a personalized travel recommendation plan through the platform. The system first generates a preliminary first recommendation plan based on Mr. Zhang's needs, but in order to better meet Mr. Zhang's needs, the first recommendation plan can be further optimized based on spatiotemporal information and voice features.

[0047] First location: the city where Mr. Zhang is currently located (e.g. Shanghai). Travel time: Mr. Zhang plans to travel to Beijing in the next three days, with the specific departure date and time. Voice features: Through the voice input when Mr. Zhang interacts with the system, the system obtains Mr. Zhang’s pronunciation habits, speaking speed and other voice features.

[0048] Second location: Beijing, as Mr. Zhang's travel destination. Weather characteristics: The system obtains the weather conditions of Beijing in the next three days, including temperature, humidity, wind speed, etc. by querying real-time weather data. Traffic characteristics: The system analyzes the traffic conditions in Beijing, including the congestion of major roads, the operating hours of public transportation, etc. Attraction information: The system sorts out the main tourist attractions in Beijing, including the opening hours of the attractions, ticket prices, and tourist reviews.

[0049] Optimize the first recommended solution: Audio guide: Based on Mr. Zhang's voice characteristics, the system optimized the audio guide part of the first recommended solution. For example, the speech speed and tone were adjusted to make the audio guide more in line with Mr. Zhang's hearing habits. At the same time, the system also provides multiple language options to meet Mr. Zhang's language preferences.

[0050] Sequence of tourist attractions: The system adjusts the sequence of tourist attractions in the first recommended plan based on the weather and traffic characteristics of Beijing. For example, outdoor attractions are arranged on days with clear weather and suitable temperatures, and indoor attractions are arranged on days with possible rain or low temperatures. In addition, the system also takes into account the traffic connections between attractions to minimize the time Mr. Zhang spends on the road.

[0051] Transportation: The system recommends the most suitable transportation method for Mr. Zhang based on his travel time and the traffic conditions in Beijing. For example, it recommends taking the subway or bus during peak hours to avoid traffic congestion; it recommends taking a taxi or booking a private car service at night or in remote areas.

[0052] After the above optimization, the system generated a more personalized, accurate and easy-to-understand target recommendation plan for Mr. Zhang. The plan includes: Detailed introduction to tourist attractions and the order of touring; personalized audio guide service; suitable transportation suggestions; real-time weather and traffic conditions reminders; other travel suggestions, such as dining, accommodation, etc.

[0053] It can be concluded from the above that this embodiment can more accurately understand and meet the personalized needs of target users by comprehensively considering spatiotemporal information and the input information (whether text or voice) of the target user on the intelligent recommendation platform. For voice information, the recommendation scheme is further optimized by extracting voice features, so that the recommendation is more in line with the user's auditory habits and preferences, thereby improving the intelligence level of travel planning recommendations and user experience.

[0054] In one embodiment of the present disclosure, determining a first user matching the target user on the intelligent recommendation platform based on the target user's personal information includes: Based on the clustering algorithm, the registered users on the intelligent recommendation platform are grouped to obtain multiple user groups; Determine the target user group based on the similarity between the personal information characteristics of each user group and the personal information characteristics of the target user; At least one user is selected from the target user group as the first user.

[0055] In this embodiment, a registered user refers to a user who has registered on the intelligent recommendation platform and provided personal information in order to receive recommendation services. The information of these users generally includes but is not limited to age, gender, geographic location, hobbies, etc.

[0056] A clustering algorithm is a data mining technique used to divide a group of data objects into several groups or clusters, so that data objects in the same group have a high degree of similarity, while data objects in different groups have a low degree of similarity. In this embodiment, a clustering algorithm is used to group registered users.

[0057] A user group is a set of users obtained through a clustering algorithm, and the users in each user group have similar personal information characteristics.

[0058] Selecting at least one user from the target user group as the first user includes: The target weight of the user is determined according to the weights of different user information, and at least one user is selected as the first user according to the target weight.

[0059] In this embodiment, the system first collects personal information of users registered on the platform, including but not limited to age, gender, geographic location, hobbies, historical behavior records, etc., and pre-processes the collected personal information, such as deduplication, cleaning, formatting, etc., to ensure the accuracy and consistency of the data. Clustering algorithms (such as K-means clustering, hierarchical clustering, etc.) are applied to group the pre-processed registered users. The clustering algorithm can divide users into multiple user groups according to the similarity of user personal information characteristics, and users in each user group have similar personal information characteristics and behavior patterns.

[0060] For the target user, extract their personal information features. Calculate the similarity between the target user's personal information features and the central features of each user group (such as using cosine similarity, Euclidean distance and other measurement methods), sort by similarity, and select the user group with the highest similarity as the target user group. In the target user group, select at least one user as the first user based on the user characteristics. Use the first user's preferences and behavior patterns and other information to provide personalized recommendation services for the target user.

[0061] From the above, it can be concluded that the embodiment of the present disclosure effectively groups registered users on the intelligent recommendation platform through a clustering algorithm, and accurately determines the target user group that matches the target user based on the similarity of personal information characteristics. Then, at least one user is selected from the group as the first user. These first users have a high degree of similarity with the target user and can more accurately reflect the preferences and needs of the target user, thereby greatly improving the personalization and accuracy of intelligent recommendations and enhancing the user experience.

[0062] In one embodiment of the present disclosure, generating a first recommendation scheme for a target user based on historical behavior information of the target user and historical behavior information of a first user includes: Determine similarities and differences between the historical behavior information of the target user and the first user; The historical behavior information of the first user is weighted based on the commonalities and differences to generate a first recommendation scheme for the target user.

[0063] In this embodiment, the common point refers to the part of the historical behavior information of the target user and the first user where the similarity is greater than the first threshold; the difference point refers to the part of the historical behavior information of the target user and the first user where the similarity is less than the second threshold; the first threshold is greater than the second threshold. The first threshold and the second threshold are two thresholds preset by the intelligent recommendation platform, the first threshold can be 0.7, and the second threshold can be 0.5.

[0064] In this embodiment, first, the system analyzes the historical behavior information of the target user and the first user. Then, the system determines the commonalities and differences in the historical behavior information of the target user and the first user. The commonalities reflect the common interests or needs of the target user and the first user. The differences reflect the personalized differences between the target user and the first user in certain aspects. Finally, the system weights the historical behavior information of the first user according to the commonalities and differences. For the commonalities, the system gives a higher weight because these parts can better reflect the real needs of the target user. For the differences, the system will weight them to different degrees according to the specific situation to balance the needs of personalized recommendations and common recommendations. Based on the weighted historical behavior information of the first user, a first recommendation plan for the target user is generated.

[0065] It can be concluded from the above that the target recommendation scheme generated by this embodiment not only includes the content that the target user is interested in, but also takes into account the personalized differences between the target user and the first user, thereby improving the accuracy and relevance of the recommendation.

[0066] In one embodiment of the present disclosure, weighting the historical behavior information of the first user based on the commonalities and differences to generate a first recommendation scheme for the target user includes: Determine a weight corresponding to each item in the historical behavior information of the first user based on the commonalities and differences; The historical behavior information of the first user is weighted based on the weight corresponding to each item to generate a first recommendation plan for the target user.

[0067] In this embodiment, based on the commonalities and differences, weights are calculated for the items in each first user's historical behavior information (such as scenic spot style, scenic spot location, means of transportation, etc.). For items with more commonalities, higher weights can be given because these items are more in line with the preferences of the target user. For items with larger differences, the weights can be adjusted according to actual conditions. If it is believed that these differences have no negative impact on the recommendation or can provide new points of interest for the target user, a certain weight can be given; if it is believed that these differences are contrary to the preferences of the target user, their weights can be reduced or ignored. According to the calculated weights, the historical behavior information of the first user is weighted. Based on the weighted historical behavior information of the first user, a first recommendation scheme for the target user is generated.

[0068] It can be concluded from the above that this embodiment calculates the weight of each item in the historical behavior information of the first user and fully considers the similarities and differences between the target user and the first user, so that the recommendation scheme is more in line with the personalized needs of the target user. At the same time, this embodiment can flexibly adjust the weight of the difference point item, which not only avoids inaccurate recommendations caused by excessive differences, but also provides the target user with an opportunity to explore new points of interest, thereby improving the accuracy of recommendations and user satisfaction.

[0069] In one embodiment of the present disclosure, determining the weight corresponding to each item in the historical behavior information of the first user based on the commonalities and differences includes: Determining a difference threshold based on the difference points; If the difference value of the item corresponding to the difference point is greater than the difference threshold, the item corresponding to the difference point is assigned a weight in the first range or the item corresponding to the difference point is excluded from the first recommendation scheme; If the difference value of the item corresponding to the difference point is less than or equal to the difference threshold, assigning the item corresponding to the difference point a weight in the second range; The weight of the first range is less than the weight of the second range.

[0070] In this embodiment, the difference value is a numerical value that quantifies the size or degree of the difference point. It reflects the degree of difference in preference between the target user and the first user on a specific project. The difference threshold is a pre-set numerical limit used to distinguish the degree of influence of the difference point on the project weight allocation. The difference point with a difference value less than this threshold will be regarded as a significant difference, otherwise it will be regarded as a small difference. The difference threshold in this embodiment is a threshold preset by the intelligent recommendation platform, and the setting of the difference threshold is related to the second threshold, and the difference threshold is less than the second threshold. For example, the difference threshold can be 50% of the second threshold. When the difference value of the difference point is greater than the difference threshold, the weight range of the corresponding project of the difference point is assigned. The weight of this range is usually low, indicating that the preference of the project is relatively different from that of the target user, so it is less important in the recommendation scheme. When the difference value of the difference point is less than or equal to the difference threshold, the weight range of the corresponding project of the difference point is assigned. The weight of this range is usually high, indicating that the preference of the project is relatively close to that of the target user, so it is more important in the recommendation scheme.

[0071] It can be concluded from the above that this embodiment can effectively distinguish the preference differences between the target user and the first user on different items by setting the difference threshold and accurately adjusting the item weights. For items with significant differences, the weights are reduced or excluded to avoid irrelevant recommendations; for items with smaller differences, the weights are appropriately increased to ensure personalized recommendations. This embodiment can significantly improve the accuracy of recommendations and user satisfaction, making the recommendation plan more in line with the actual needs of the target user.

[0072] In one embodiment of the present disclosure, determining the weight corresponding to each item in the historical behavior information of the first user based on the commonalities and differences further includes: Determine a similarity threshold based on the commonalities; If the similarity of the items corresponding to the same point is greater than the similarity threshold, the items corresponding to the same point are directly added to the first recommendation scheme; If the similarity of the items corresponding to the same point is less than or equal to the similarity threshold, the items corresponding to the same point are assigned a weight in the third range.

[0073] In this embodiment, the system sets one or more difference thresholds based on the historical behavior information differences between the target user and the first user. These thresholds are used to determine which items have significant differences and which differences are acceptable or small. Then, the system compares the difference value of each item with the difference threshold. If the difference value of an item is greater than the set threshold, it means that the item may not be very relevant to the target user or there is a large preference difference, so the system will reduce the weight of the item or even choose to exclude it from the recommendation scheme completely. On the contrary, if the difference value of an item is less than or equal to the threshold, it means that the item is closer to the target user's preference, and the system will moderately increase the weight of the item. In this way, the system can effectively distinguish the preference differences between the target user and the first user on different items, so as to more accurately determine which items should be recommended to the target user.

[0074] From the above, it can be concluded that this embodiment further refines the allocation strategy of project weights by setting a similarity threshold. For highly similar projects (exceeding the threshold), they are directly added to the recommendation scheme to ensure that the core preferences are met; for projects with lower similarity, appropriate weights are given to balance personalization and breadth. This embodiment can not only improve the accuracy of recommendations, but also enhance the diversity of recommendations and user satisfaction.

[0075] Corresponding to the tourist attraction recommendation method based on big data in the above embodiment, Figure 2 This is a structural block diagram of a tourist attraction recommendation system based on big data provided by an embodiment of the present disclosure. For ease of explanation, only the parts related to the embodiment of the present disclosure are shown. Figure 2 The tourist attraction recommendation system 20 based on big data is applied to an intelligent recommendation platform, including: a matching module 21, a first determination module 22 and a solution recommendation module 23.

[0076] The matching module 21 is used to determine the first user matching the target user on the intelligent recommendation platform based on the personal information of the target user; A first determination module 22, configured to generate a first recommendation scheme for the target user based on the historical behavior information of the target user and the historical behavior information of the first user; The solution recommendation module 23 is used to optimize the first recommended solution of the target user based on the spatiotemporal information to obtain a target recommended solution; wherein the spatiotemporal information includes time information and space information.

[0077] In one embodiment of the present disclosure, the solution recommendation module 23 is specifically used to: Based on the spatiotemporal information and the input information of the target user on the intelligent recommendation platform, the first recommendation plan for the target user is optimized to obtain the target recommendation plan.

[0078] In one embodiment of the present disclosure, the input information includes text information or voice information; The solution recommendation module 23 is specifically used for: If the input information is text information, the first recommendation scheme for the target user is optimized based on the spatiotemporal information and the text information to obtain a target recommendation scheme; If the input information is voice information, the voice features are determined based on the voice information, and the first recommendation scheme for the target user is optimized based on the spatiotemporal information and the voice features to obtain a target recommendation scheme.

[0079] In one embodiment of the present disclosure, the matching module 21 is specifically used for: Based on the clustering algorithm, the registered users on the intelligent recommendation platform are grouped to obtain multiple user groups; Determine the target user group based on the similarity between the personal information characteristics of each user group and the personal information characteristics of the target user; At least one user is selected from the target user group as the first user.

[0080] In one embodiment of the present disclosure, the first determining module 22 is specifically configured to: Determine similarities and differences between the historical behavior information of the target user and the first user; The historical behavior information of the first user is weighted based on the commonalities and differences to generate a first recommendation scheme for the target user.

[0081] In one embodiment of the present disclosure, the first determining module 22 is specifically configured to: Determine a weight corresponding to each item in the historical behavior information of the first user based on the commonalities and differences; The historical behavior information of the first user is weighted based on the weight corresponding to each item to generate a first recommendation plan for the target user.

[0082] In one embodiment of the present disclosure, the first determining module 22 is specifically configured to: Determining a difference threshold based on the difference points; If the difference value of the item corresponding to the difference point is greater than the difference threshold, the item corresponding to the difference point is assigned a weight in the first range or the item corresponding to the difference point is excluded from the first recommendation scheme; If the difference value of the item corresponding to the difference point is less than or equal to the difference threshold, assigning the item corresponding to the difference point a weight in the second range; The weight of the first range is less than the weight of the second range.

[0083] See also Figure 3 , Figure 3 A schematic block diagram of an electronic device provided by an embodiment of the present disclosure. Figure 3 The electronic device 300 in the embodiment shown may include: one or more processors 301, one or more input devices 302, one or more output devices 303 and one or more memories 304. The processors 301, input devices 302, output devices 303 and memories 304 communicate with each other via a communication bus 305. The memory 304 is used to store computer programs, which include program instructions. The processor 301 is used to execute the program instructions stored in the memory 304. The processor 301 is configured to call the program instructions to execute the functions of each module / unit in the above-mentioned device embodiments, such as Figure 2 The functions of modules 21 to 23 are shown.

[0084] It should be understood that in the embodiment of the present disclosure, the processor 301 may be a central processing unit (CPU), and the processor may also be other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc.

[0085] The input device 302 may include a touch panel, a fingerprint collection sensor (for collecting the user's fingerprint information and fingerprint direction information), a microphone, etc., and the output device 303 may include a display (LCD, etc.), a speaker, etc.

[0086] The memory 304 may include a read-only memory and a random access memory, and provide instructions and data to the processor 301. A portion of the memory 304 may also include a non-volatile random access memory. For example, the memory 304 may also store information about the device type.

[0087] In a specific implementation, the processor 301, input device 302, and output device 303 described in the embodiments of the present disclosure can execute the implementation methods described in the first and second embodiments of the tourist attraction recommendation method based on big data provided in the embodiments of the present disclosure, and can also execute the implementation methods of the electronic device described in the embodiments of the present disclosure, which will not be repeated here.

[0088] In another embodiment of the present disclosure, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores a computer program, wherein the computer program includes program instructions, and when the program instructions are executed by the processor, all or part of the processes in the above-mentioned embodiment method are implemented, and the computer program can also be completed by instructing the relevant hardware through the computer program. The computer program can be stored in a computer-readable storage medium, and when the computer program is executed by the processor, the steps of each of the above-mentioned method embodiments can be implemented. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form, etc. The computer-readable medium can include: any entity or device capable of carrying computer program code, recording medium, U disk, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), electric carrier signal, telecommunication signal and software distribution medium, etc.

[0089] The computer-readable storage medium may be an internal storage unit of the electronic device of any of the aforementioned embodiments, such as a hard disk or memory of the electronic device. The computer-readable storage medium may also be an external storage device of the electronic device, such as a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (SecureDigital, SD) card, a flash card (Flash Card), etc. equipped on the electronic device. Furthermore, the computer-readable storage medium may also include both an internal storage unit of the electronic device and an external storage device. The computer-readable storage medium is used to store computer programs and other programs and data required by the electronic device. The computer-readable storage medium may also be used to temporarily store data that has been output or is to be output.

[0090] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of the two. In order to clearly illustrate the interchangeability of hardware and software, the composition and steps of each example have been generally described in terms of function in the above description. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this disclosure.

[0091] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the electronic devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0092] In the several embodiments provided in the present application, it should be understood that the disclosed electronic devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic, for example, the division of units is only a logical function division, and there may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interfaces or units, or it can be an electrical, mechanical or other form of connection.

[0093] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the embodiments of the present disclosure.

[0094] In addition, each functional unit in each embodiment of the present disclosure may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0095] The above are only specific embodiments of the present disclosure, but the protection scope of the present disclosure is not limited thereto. Any technician familiar with the technical field can easily think of various equivalent modifications or replacements within the technical scope disclosed in the present disclosure, and these modifications or replacements should be included in the protection scope of the present disclosure. Therefore, the protection scope of the present disclosure should be based on the protection scope of the claims.

Claims

1. A tourist attraction recommendation method based on big data, characterized in that: Applied to intelligent recommendation platforms, including: Determining a first user on the intelligent recommendation platform that matches the target user based on the target user's personal information; generating a first recommendation scheme for the target user based on the historical behavior information of the target user and the historical behavior information of the first user; The first recommendation scheme for the target user is optimized based on the spatiotemporal information to obtain a target recommendation scheme; wherein the spatiotemporal information includes time information and space information.

2. The tourist attraction recommendation method based on big data according to claim 1, characterized in that: The step of optimizing the first recommendation scheme for the target user based on the spatiotemporal information to obtain the target recommendation scheme includes: Based on the spatiotemporal information and the input information of the target user on the intelligent recommendation platform, the first recommendation scheme for the target user is optimized to obtain a target recommendation scheme.

3. The tourist attraction recommendation method based on big data as claimed in claim 2, characterized in that: The input information includes text information or voice information; The step of optimizing the first recommendation scheme for the target user based on the spatiotemporal information and the input information of the target user on the intelligent recommendation platform to obtain a target recommendation scheme includes: If the input information is text information, optimizing the first recommendation scheme for the target user based on the spatiotemporal information and the text information to obtain a target recommendation scheme; If the input information is voice information, voice features are determined based on the voice information, and the first recommendation scheme for the target user is optimized based on the spatiotemporal information and the voice features to obtain a target recommendation scheme.

4. The tourist attraction recommendation method based on big data according to claim 1, characterized in that: The determining, based on the personal information of the target user, a first user matching the target user on the intelligent recommendation platform comprises: Based on the clustering algorithm, the registered users on the intelligent recommendation platform are grouped to obtain multiple user groups; Determine the target user group based on the similarity between the personal information characteristics of each user group and the personal information characteristics of the target user; At least one user is selected from the target user group as a first user.

5. The tourist attraction recommendation method based on big data according to claim 1, characterized in that: The generating a first recommendation scheme for the target user based on the historical behavior information of the target user and the historical behavior information of the first user includes: Determine similarities and differences between the historical behavior information of the target user and the first user; The historical behavior information of the first user is weighted based on the commonalities and the differences to generate a first recommendation scheme for the target user.

6. The method for recommending tourist attractions based on big data according to claim 5, characterized in that: The step of weighting the historical behavior information of the first user based on the commonalities and the differences to generate a first recommendation scheme for the target user includes: Determine a weight corresponding to each item in the historical behavior information of the first user based on the common points and the differences; The historical behavior information of the first user is weighted based on the weight corresponding to each item to generate a first recommendation plan for the target user.

7. The tourist attraction recommendation method based on big data according to claim 6, characterized in that: The determining, based on the common points and the differences, a weight corresponding to each item in the historical behavior information of the first user includes: determining a difference threshold based on the difference point; If the difference value of the item corresponding to the difference point is greater than the difference threshold, assigning the item corresponding to the difference point a weight in a first range or excluding the item corresponding to the difference point from the first recommendation scheme; If the difference value of the item corresponding to the difference point is less than or equal to the difference threshold, assigning the item corresponding to the difference point a weight in the second range; The weight of the first range is less than the weight of the second range.

8. The tourist attraction recommendation system based on big data is characterized by: Applied to intelligent recommendation platforms, including: A matching module, configured to determine a first user on the intelligent recommendation platform that matches the target user based on the target user's personal information; A first determination module, configured to generate a first recommendation scheme for the target user based on the historical behavior information of the target user and the historical behavior information of the first user; The solution recommendation module is used to optimize the first recommended solution of the target user based on the spatiotemporal information to obtain a target recommended solution; wherein the spatiotemporal information includes time information and space information.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

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