Service recommendation scheme determination method and apparatus, computer device, and storage medium

Abstract

The application relates to a service recommendation scheme determination method and device, computer equipment and a storage medium. The method comprises the following steps: acquiring real-time data of a user; identifying a service scene in which the user is located according to the real-time data; matching each key experience point corresponding to the user and the service scene in a pre-established user experience database, wherein the user experience database stores each preset service scene in which each preset user is located and each preset key experience point corresponding to each preset service scene; evaluating the attention degree of the user to each target key experience point, wherein each key experience point comprises each target key experience point; and determining a service recommendation scheme of the service scene for the user according to the attention degrees. The method of the application can determine the service recommendation scheme of the user, and effectively improve the efficiency and service effect of recommending the service recommendation scheme to the user.

Description

Technical Field

[0001] This application relates to the field of communication technology, and in particular to a method, apparatus, computer equipment, and storage medium for determining a service recommendation scheme. Background Technology

[0002] With the development of communication technology and the improvement of living standards, when users are in a certain service scenario, such as a business trip, a food service scenario, or a travel service scenario, they often search for service recommendations in this scenario first, in order to improve the user experience in this service scenario.

[0003] A user may experience multiple points of interaction within a given service scenario, but only the key points have a core impact on the user experience. However, when multiple key points exist within a service scenario, multiple service recommendation options arise, leading to inefficiency in recommending services to the user. Summary of the Invention

[0004] Therefore, it is necessary to provide a service recommendation scheme determination method, apparatus, computer equipment, and storage medium that can improve the efficiency of recommending service schemes to users, in order to address the above-mentioned technical problems.

[0005] A method for determining a service recommendation scheme, the method comprising:

[0006] Obtain real-time user data;

[0007] Based on the real-time data, the service scenario in which the user is located can be identified;

[0008] In a pre-established user experience database, key experience points corresponding to the user and the service scenario are matched. The user experience database stores the preset service scenarios where each preset user is located and the preset key experience points corresponding to each preset service scenario.

[0009] Assess the user’s attention to each target key experience point, where each key experience point includes each target key experience point;

[0010] Based on the level of attention mentioned above, a service recommendation scheme for the user in the service scenario is determined.

[0011] In one embodiment, the real-time data includes at least two of the following: time data, video data obtained with user authorization, audio data, and location data;

[0012] The step of identifying the service scenario in which the user is located based on the real-time data includes:

[0013] Visual recognition is performed based on the video data, and the service scenario in which the user is located is perceived based on the time data, the audio data, and the location data.

[0014] In one embodiment, matching key experience points corresponding to the user and the service scenario in a pre-established user experience database includes:

[0015] User matching is performed in the user experience database;

[0016] When the user is matched, the target service scenario corresponding to the user is matched in the user experience database, and each preset key experience point corresponding to the target service scenario is determined as the key experience point.

[0017] If no user is matched, similar users are identified in the user experience database based on the real-time data. The target service scenario corresponding to the similar user is matched in the user experience database, and each preset key experience point corresponding to the target service scenario is identified as the key experience point.

[0018] In one embodiment, when the user is matched, the step of matching the target service scenario corresponding to the user in the user experience database, and determining each preset key experience point corresponding to the target service scenario as the key experience point, includes:

[0019] When the user is matched, the service scenario is matched in the user experience database among the preset service scenarios in which the user is located;

[0020] When the service scenario is matched, the matched service scenario is taken as the target service scenario, and each preset key experience point corresponding to the service scenario is determined as each of the key experience points.

[0021] When no service scenario is matched, a similar service scenario is identified from the preset service scenarios in the user experience database. The similar service scenario is taken as the target service scenario, and the preset key experience points corresponding to the similar service scenario are identified as the key experience points.

[0022] In one embodiment, the user experience database is established by means of:

[0023] Obtain the preset service scenarios for each preset user;

[0024] Based on the preset knowledge graph and the scene tags corresponding to the preset service scenario, the preset key experience points corresponding to the preset service scenario are obtained. The knowledge graph stores scene tags and key experience points, and there is an association between the scene tags and the key experience points.

[0025] A user experience database is established based on the preset service scenarios and preset key experience points of each preset user.

[0026] In one embodiment, the user experience database is established by means of:

[0027] Obtain application data for each preset user in a preset service scenario, wherein the application data includes at least one of the preset user's search data and comment data;

[0028] Analyze the data of each application, extract the key experience points corresponding to each preset user, and obtain the preset key experience points;

[0029] A user experience database is established based on the preset service scenarios and preset key experience points of each preset user.

[0030] In one embodiment, the method for determining each of the target key experience points includes:

[0031] Based on the real-time data, identify the current key experience point corresponding to the user, and each key experience point includes the current key experience point;

[0032] The key experience points are sorted to obtain the sorted key experience points;

[0033] Among the sorted key experience points, the current key experience point is determined, and each key experience point after the current key experience point is taken as the target key experience point.

[0034] In one embodiment, assessing the user's attention to each target key experience point includes:

[0035] When a user is matched, the user's historical experience data is obtained; otherwise, the historical experience data of similar users is obtained.

[0036] Identify each historical key experience point corresponding to the historical experience data, and calculate the historical attention corresponding to each historical key experience point;

[0037] Based on the historical attention levels mentioned above, the user's attention to each target key experience point is assessed.

[0038] In one embodiment, determining the service recommendation scheme for the user based on each of the stated levels of attention includes:

[0039] Based on the level of attention mentioned above, the key experience points of each target are sorted to obtain the sorted key experience points of each target.

[0040] Based on the sorted key experience points of each target, a service recommendation scheme for the user is generated for the service scenario.

[0041] In one embodiment, after determining the service recommendation scheme for the user based on each of the stated attention levels, the method further includes:

[0042] The service effectiveness of the service recommendation scheme is evaluated based on the experience duration of the service recommendation scheme or the user's experience feedback.

[0043] A service recommendation scheme determination apparatus, the apparatus comprising:

[0044] The acquisition module is used to acquire real-time user data;

[0045] The identification module is used to identify the service scenario in which the user is located based on the real-time data.

[0046] The matching module is used to match key experience points corresponding to the user and the service scenario in a pre-established user experience database. The user experience database stores the preset service scenarios where each preset user is located and the preset key experience points corresponding to each preset service scenario.

[0047] The evaluation module is used to evaluate the user's attention to each target key experience point, and each key experience point includes each target key experience point;

[0048] The recommendation module is used to determine the service recommendation scheme for the user based on the aforementioned attention levels.

[0049] A computer device includes a memory and a processor, the memory storing a computer program, and the processor executing the computer program to implement the steps of the above-described service recommendation scheme determination method.

[0050] A computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the above-described service recommendation scheme determination method.

[0051] The aforementioned service recommendation scheme determination method, apparatus, computer equipment, and storage medium acquire user real-time data; identify the user's service scenario based on the real-time data; match key experience points corresponding to the user and service scenario in a pre-established user experience database, the user experience database storing preset service scenarios for each preset user and preset key experience points corresponding to each preset service scenario; evaluate the user's attention to each target key experience point, each key experience point including the target key experience point; and determine the service recommendation scheme for the user based on each attention level. Using the method of this application embodiment, by storing each user, service scenario, and key experience point corresponding to the service scenario in a pre-established user experience database, and combining it with continuously acquired user real-time data, the method identifies the user's service scenario and generates a service recommendation scheme for the user. This enables targeted determination of service recommendation schemes for users, effectively improving the efficiency and service effectiveness of recommending service schemes to users. Attached Figure Description

[0052] Figure 1 This is an application environment diagram of the service recommendation scheme determination method in one embodiment;

[0053] Figure 2 This is a flowchart illustrating a service recommendation scheme determination method in one embodiment;

[0054] Figure 3 A flowchart of a service recommendation scheme determination method in a specific embodiment;

[0055] Figure 4 This is a structural block diagram of a service recommendation scheme determination device in one embodiment;

[0056] Figure 5 This is an internal structural diagram of a computer device in one embodiment;

[0057] Figure 6 This is a diagram of the internal structure of a computer device in another embodiment. Detailed Implementation

[0058] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0059] In one embodiment, the service recommendation scheme determination method provided in this application can be applied in an environment that simultaneously involves terminal 102 and server 104, such as... Figure 1As shown. Terminal 102 can communicate with server 104 via a network or protocol. Specifically, server 104 can obtain real-time user data through terminal 102; based on the real-time data, identify the service scenario in which the user is located; match key experience points corresponding to the user and service scenario in a pre-established user experience database, the user experience database storing preset service scenarios in which each preset user is located and preset key experience points corresponding to each preset service scenario; evaluate the user's attention to each target key experience point, each key experience point including each target key experience point; and determine the service recommendation scheme for the user based on the attention level.

[0060] In one embodiment, the service recommendation scheme determination method provided in this application may only involve terminal 102 or server 104 in its application environment. Specifically, server 104 or terminal 102 can directly obtain real-time user data; identify the service scenario in which the user is located based on the real-time data; match key experience points corresponding to the user and service scenario in a pre-established user experience database, the user experience database storing preset service scenarios in which each preset user is located and preset key experience points corresponding to each preset service scenario; evaluate the user's attention to each target key experience point, each key experience point including each target key experience point; and determine the service recommendation scheme for the user based on each level of attention.

[0061] The terminal 102 can be, but is not limited to, various personal computers, laptops, smartphones, tablets and portable wearable devices, and the server 104 can be implemented by a standalone server or a server cluster consisting of multiple servers.

[0062] In one embodiment, such as Figure 2 As shown, a method for determining a service recommendation scheme is provided, which is then applied to... Figure 1 Taking terminal 102 and / or server 104 as examples, the following is an explanation:

[0063] Step S202: Obtain the user's real-time data.

[0064] In one embodiment, a service scenario refers to the actual tangible facility where the service is executed, delivered, and consumed. It plays a crucial role in shaping user expectations, influencing user experiences, and differentiating service organizations. Within a given service scenario, due to differences in user needs and concerns, service recommendation schemes also differ between users, with each recommendation scheme corresponding to a specific user.

[0065] In one embodiment, the user's main needs and key concerns in a service scenario are referred to as key experience points. Key experience points include at least one of sensory experience, interactive experience, and emotional experience. Specifically, service scenarios can be classified according to their purpose, mainly including: self-service scenarios, interactive service scenarios, and remote service scenarios. Self-service scenarios involve only the user, such as internet service scenarios and bank self-service scenarios; interactive services involve both the user and service staff, such as food service scenarios and hair salon scenarios; and remote services involve only service staff, such as public utility service scenarios and automated voice information service scenarios. Based on the classification of service scenarios, key experience points can be specific processes or nodes within the service scenario.

[0066] In one embodiment, the service scenario in which the user is located can be determined by acquiring the user's real-time data, and a service recommendation scheme can be generated for the user. The real-time data includes at least two of the following: time data, video data authorized by the user, audio data, and location data. Specifically, the real-time data can be data from the user's terminal or data collected by data acquisition devices in the user's service scenario.

[0067] In one embodiment, before acquiring the user's real-time data, the method further includes: identifying the user. When the real-time data is data from a user's terminal, identification can be performed based on the terminal's identifier or biometric data collected by the user's terminal. Specifically, when a user adds or follows a service account corresponding to their service scenario through their terminal, the terminal identifier is acquired, and the user is identified based on the terminal identifier. Alternatively, biometric data can be acquired through the user's terminal, including at least one of the user's facial data, fingerprint data, vein data, and iris data, for biometric identification to determine the corresponding user. When the real-time data is data collected by data acquisition devices in the user's service scenario, these data acquisition devices include image acquisition devices and positioning acquisition devices, including but not limited to various types of cameras, webcams, and positioning devices. Specifically, multiple users can be identified using image data collected by the image acquisition device, and a specific user can be located among multiple users using positioning data collected by the positioning acquisition device.

[0068] Step S204: Identify the service scenario in which the user is located based on real-time data.

[0069] In one embodiment, the service scenario in which the user is located can be identified based on the user's real-time data. This identification is made by combining multiple real-time data sources, not just one. Specifically, visual recognition is performed based on video data, and the most likely initial service scenario is determined based on time data, audio data, and location data. As more real-time user data is acquired, the initial service scenario is adjusted to perceive the user's current service scenario. For example, by combining video data, time data, and location data, if the current time is close to mealtime, the user's current service scenario is perceived as a food service scenario. As more real-time data is acquired, the service scenario is adjusted and confirmed accordingly.

[0070] Step S206: In the pre-established user experience database, match each key experience point corresponding to the user and service scenario. The user experience database stores the preset service scenarios where each preset user is located and each preset key experience point corresponding to each preset service scenario.

[0071] In one embodiment, within a defined service scenario, due to differences in user needs and concerns, key experience points within the service scenario can be pre-stored for different users, establishing a user experience database. The stored users are referred to as preset users, the service scenarios where the preset users reside are called preset service scenarios, and the key experience points within the preset service scenarios are called preset key experience points. In other words, the user experience database stores the preset service scenarios for each preset user and the corresponding preset key experience points for each preset service scenario. Specifically, preset users can be set and categorized based on factors such as gender, age group, and education level, and the terminal identifier or biometric data of the preset user's device can be stored accordingly. Preset service scenarios can be set according to the service scenario classification. These preset service scenarios can be obtained from publicly available databases or manually set service scenarios. Preset service scenarios include preset service scenario information, such as time information, video information, audio information, and location information.

[0072] In one embodiment, after identifying the service scenario in which the user is located, key experience points corresponding to the user and service scenario are matched in a pre-established user experience database. This can be achieved by first matching users among preset users in the user experience database. When a user is matched, service scenario matching is performed within the preset service scenarios in which the user resides. When a service scenario is matched, key experience points corresponding to the user and service scenario are determined. If no user or service scenario is matched, similar users or similar service scenarios can be identified based on real-time data to improve the accuracy of the identified key experience points.

[0073] Step S208: Assess user attention to each target key experience point, including each target key experience point.

[0074] In one embodiment, target key experience points refer to the key experience points that the user has not yet experienced among the identified key experience points. Specifically, after determining the key experience points in the user's service scenario, the current key experience point of the user can be determined based on the acquired real-time data. Analysis is then performed on the user to determine the chronological order of their experience with each key experience point. Based on this chronological order, the key experience points are sorted to determine the key experience points following the current one, which are the target key experience points. After determining the target key experience points, only the user's attention to each target key experience point can be evaluated to improve evaluation efficiency.

[0075] In one embodiment, assessing user attention to each key objective experience point can be done using relevant historical experience data. Specifically, user matching is performed in a user experience database. When a user is matched, their historical experience data is retrieved; otherwise, historical experience data of similar users is retrieved. More specifically, sentiment analysis can be performed on the historical experience data, dividing it into positive and negative historical experience data. When assessing user attention to each key objective experience point, both positive and negative historical experience data are combined. For example, in a food service scenario, some users prioritize taste and have a high tolerance for the environment, while others prioritize the environment and have moderate requirements for taste. The assessment combines multiple data sources.

[0076] Step S210: Determine the service recommendation scheme for users based on each level of attention.

[0077] In one embodiment, a service recommendation plan for the user can be determined based on the attention level corresponding to each key experience point. This service recommendation plan can be the user's experience flow, designed to ensure that the user, when experiencing the service according to the recommendation plan, achieves or exceeds positive historical experience data as much as possible, while avoiding or reducing negative historical experience data.

[0078] In one embodiment, after determining the service recommendation scheme for the user in a service scenario, the method further includes: evaluating the service effectiveness of the service recommendation scheme based on the experience duration of the service recommendation scheme or the user's experience feedback. Specifically, the experience duration refers to the total time it takes for the user to complete the service recommendation scheme. When the experience duration is less than the historical average duration, the service recommendation scheme is determined to have a positive effect. The historical average duration refers to the average time taken to complete historical service recommendation schemes corresponding to historical service scenarios in multiple historical service scenarios similar to the service scenario. Specifically, user experience feedback is obtained. Experience feedback can be rating feedback; when the rating is higher than a benchmark score, the service recommendation scheme is determined to have a positive effect. Experience feedback can also be evaluation feedback. Semantic analysis is performed on the experience feedback; when the evaluation is positive, the service recommendation scheme is determined to have a positive effect. After evaluating the service effectiveness of the service recommendation scheme, the service recommendation scheme is stored as the user's historical experience data for iterative optimization when generating service recommendation schemes for the user's service scenario in subsequent iterations, thereby improving service effectiveness and recommendation efficiency.

[0079] In the above-described method for determining service recommendation schemes, real-time user data is acquired; based on the real-time data, the service scenario in which the user is located is identified; in a pre-established user experience database, key experience points corresponding to the user and service scenario are matched; the user experience database stores preset service scenarios in which each preset user is located and preset key experience points corresponding to each preset service scenario; the user's attention to each target key experience point is evaluated; each key experience point includes each target key experience point; and based on the attention level, a service recommendation scheme for the user in the service scenario is determined. By using the method described in the above embodiment, by storing each user, service scenario, and key experience point corresponding to the service scenario in a pre-established user experience database, and combining this with continuously acquired real-time user data to identify the user's service scenario, a service recommendation scheme for the user can be generated. This allows for targeted determination of service recommendation schemes for users, effectively improving the efficiency and service effectiveness of recommending service schemes to users.

[0080] In one embodiment, step S206 involves matching key experience points corresponding to users and service scenarios in a pre-established user experience database, including:

[0081] Step S302: Perform user matching in the user experience database.

[0082] In one embodiment, user matching is performed in a user experience database to determine whether the identified user is a preset user in the database. This matching can be based on real-time data or on factors such as the user's terminal identifier or biometric data.

[0083] Step S304: When a user is matched, the target service scenario corresponding to the user is matched in the user experience database, and each preset key experience point corresponding to the target service scenario is determined as a key experience point.

[0084] In one embodiment, when a user is determined to be a preset user in the user experience database, a user is determined to be matched. Then, service scenario matching is performed in the user experience database. The service scenario corresponding to the matched user is called the target service scenario, and each preset key experience point corresponding to the target service scenario is determined as a key experience point.

[0085] Step S306: When no user is matched, based on real-time data, identify similar users in the user experience database, match the target service scenario corresponding to the similar user in the user experience database, and determine each preset key experience point corresponding to the target service scenario as a key experience point.

[0086] In one embodiment, when it is determined that the user is not a preset user in the user experience database, it is determined that no user has been matched. Then, based on real-time data, preset users similar to the user are determined in the user experience database, also known as similar users. Similar users can be those similar to the user in terms of gender, age group, education level, etc. After determining similar users, service scenario matching is performed in the user experience database. The service scenario corresponding to the matched similar user is called the target service scenario, and the preset key experience points corresponding to the target service scenario are determined as key experience points. When no user is matched, a new user profile is created for that user, and information such as the user's service scenario and key experience points is stored in the user experience database.

[0087] In one embodiment, when a user is matched in step S304, the target service scenario corresponding to the user is matched in the user experience database, and each preset key experience point corresponding to the target service scenario is determined as a key experience point, including:

[0088] Step S402: When a user is matched, the service scenario is matched in the user experience database among the preset service scenarios where the user is located.

[0089] In one embodiment, when a user is determined to be a preset user in the user experience database, a user match is determined. Then, service scenario matching is performed in the user experience database. It should be noted that during service scenario matching, there are cases where a service scenario is matched and cases where no service scenario is matched. Specifically, the service scenario information contained in the service scenario can be compared with the preset scenario information contained in the preset scenario to complete the service scenario matching. The service scenario information includes time information, video information, audio information, and location information, etc.

[0090] Step S404: When a service scenario is matched, the matched service scenario is taken as the target service scenario, and the preset key experience points corresponding to the service scenario are determined as key experience points.

[0091] In one embodiment, when the service scenario is determined to be a preset service scenario in the user experience database, a matching service scenario is determined. The matched service scenario is then taken as the target service scenario, and the preset key experience points corresponding to the service scenario are determined as key experience points.

[0092] Step S406: When no service scenario is matched, identify similar service scenarios from the preset service scenarios in the user experience database, take the similar service scenario as the target service scenario, and determine the preset key experience points corresponding to the similar service scenario as key experience points.

[0093] In one embodiment, when it is determined that the service scenario is not a preset service scenario in the user experience database, it is determined that no service scenario has been matched. Then, based on real-time data and service scenario information, a similar service scenario is determined in the user experience database. The similar service scenario can be a preset service scenario with largely the same service scenario information. After determining the similar service scenario, it is used as the target service scenario, and the preset key experience points corresponding to the target service scenario are determined as key experience points. When no service scenario is matched, the service scenario, key experience points, and other information are stored in the user experience database.

[0094] In one embodiment, the method for establishing the user experience database in step S206 includes:

[0095] Step S502: Obtain the preset service scenarios for each preset user.

[0096] In one embodiment, preset users can be set and categorized based on factors such as gender, age group, and education level. Preset service scenarios can be service scenarios obtained from a publicly available database or manually set service scenarios. Specifically, the preset service scenarios for each preset user are obtained.

[0097] Step S504: Based on the preset knowledge graph and the scene tags corresponding to the preset service scenarios, obtain the preset key experience points corresponding to the preset service scenarios. The knowledge graph stores scene tags and key experience points, and there is a correlation between scene tags and key experience points.

[0098] In one embodiment, a knowledge graph is a series of various graphs displaying the development process and structural relationships of knowledge, capable of visually describing the interrelationships between service scenarios and corresponding key experience points. The knowledge graph stores scenario tags and key experience points, with associations existing between them. A scenario tag refers to a type tag corresponding to a service scenario. That is, in a preset knowledge graph, preset service scenarios are stored as corresponding scenario tags; common scenario tags could be food, movies, hotels, transportation, etc. A scenario tag can be considered entity 1 in the knowledge graph, and a corresponding key experience point can be considered entity 2, with an association existing between entity 1 and entity 2. Specifically, by inputting the scenario tag corresponding to a preset service scenario into the preset knowledge graph, preset key experience points corresponding to the preset service scenario can be obtained.

[0099] Step S506: Based on the preset service scenarios and preset key experience points of each preset user, establish a user experience database.

[0100] In one embodiment, each preset user, preset service scenario, and key experience points corresponding to the preset service scenario are stored accordingly to establish a user experience database.

[0101] In one embodiment, the method for establishing the user experience database in step S206 includes:

[0102] Step S602: Obtain application data of each preset user in the preset service scenario. The application data includes at least one of the preset user's search data and comment data.

[0103] In one embodiment, the application refers to various software and mini-programs used by the user, and the application data includes at least one of the search data and comment data of each preset user. Specifically, the application data of each preset user in a preset service scenario is obtained. The obtained application data is data that has been authorized by the user or is publicly available.

[0104] Step S604: Analyze the data of each application, extract the key experience points corresponding to each preset user, and obtain the preset key experience points.

[0105] In one embodiment, data from each application is analyzed to extract key experience points corresponding to each preset user. Specifically, when the application data is search data, key experience points are extracted based on the keywords corresponding to the search data. When the application data is comment data, semantic analysis is performed on the comment data to determine positive and negative comment data, and the corresponding key experience points are summarized to obtain preset key experience points.

[0106] Step S606: Establish a user experience database based on the preset service scenarios and preset key experience points for each preset user.

[0107] In one embodiment, each preset user, preset service scenario, and key experience points corresponding to the preset service scenario are stored accordingly to establish a user experience database.

[0108] In one embodiment, the method for determining each target key experience point in step S208 includes:

[0109] Step S702: Based on real-time data, identify the current key experience points corresponding to the user. Each key experience point includes the current key experience point.

[0110] In one embodiment, based on the acquired real-time data, the key experience point where the user is currently located in the service scenario is identified, referred to as the current key experience point. The current key experience point is one of the determined key experience points. For example, in a hotel service scenario, the hotel system displays that the user has checked in; based on this, the user's current key experience point can be determined.

[0111] Step S704: Sort the key experience points to obtain the sorted key experience points.

[0112] In one embodiment, the key experience points in the service scenario can be sorted according to the order of their experience time to obtain the sorted key experience points. That is, in the service scenario, the user will experience the key experience points in the order they are sorted.

[0113] Step S706: Among the sorted key experience points, determine the current key experience point, and take the key experience points after the current key experience point as the target key experience points.

[0114] In one embodiment, among the sorted key experience points, the position of the current key experience point is determined, and the key experience points following the current key experience point are designated as target key experience points. Evaluation efficiency is improved by assessing only the user's attention to each target key experience point.

[0115] In one embodiment, step S208 assesses the user's attention to each target key experience point, including:

[0116] Step S802: When a user is matched, obtain the user's historical experience data; otherwise, obtain the historical experience data of similar users.

[0117] In one embodiment, user matching is performed in a user experience database. When a user is matched, the user's historical experience data stored in the user experience database is retrieved. When no user is matched, the historical experience data of similar users stored in the user experience database is retrieved.

[0118] Step S804: Determine each historical key experience point corresponding to the historical experience data, and calculate the historical attention corresponding to each historical key experience point.

[0119] In one embodiment, historical experience data can be analyzed to identify key experience points corresponding to the historical experience data, referred to as historical key experience points. Attention level refers to the degree of user attention to these key experience points. The attention level of historical key experience points can be determined based on their frequency of occurrence or the effects they produce in the historical experience data. Specifically, for a given historical key experience point, the higher its frequency of occurrence in the user's historical experience data, and the higher the level of at least one of its positive or negative effects, the higher its attention level. Positive effects can be determined by the number of positive reviews, negative effects by the number of negative reviews, and the overall effect can be determined based on the sum of the number of positive and negative reviews.

[0120] Step S806: Based on historical attention levels, assess user attention to each target key experience point.

[0121] In one embodiment, based on the historical attention corresponding to each historical key experience point and combined with information on the similarity between each historical key experience point and each target key experience point, the user's attention to each target key experience point is evaluated.

[0122] In one embodiment, step S210 determines a service recommendation scheme for the user based on each level of attention, including:

[0123] Step S902: Sort the key experience points of each target according to their level of attention to obtain the sorted key experience points of each target.

[0124] In one embodiment, the key experience points of each target can be sorted according to their level of attention, from highest to lowest, to obtain the sorted key experience points of each target.

[0125] Step S904: Based on the sorted key experience points of each target, generate a service recommendation scheme for the user based on the service scenario.

[0126] In one embodiment, a service recommendation scheme for users is generated based on the sorted key experience points of each target, thereby taking into account the attention of each key experience point and improving the recommendation efficiency and service effect of the service recommendation scheme.

[0127] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and one specific embodiment. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0128] like Figure 3 The diagram shows a flowchart of a service recommendation scheme determination method in a specific embodiment. Specifically, the steps of the service recommendation scheme determination method are as follows:

[0129] Obtain the preset service scenarios for each preset user. The preset service scenarios can be service scenarios obtained from a public database, or service scenarios set manually, etc.

[0130] Based on the preset knowledge graph and the scene tags corresponding to the preset service scenarios, such as food, movies, hotels, and transportation, the preset key experience points corresponding to the preset service scenarios are obtained.

[0131] Obtain authorized application data for each preset user in the preset service scenario. When the application data is review data, perform semantic analysis on the review data to determine the items that users are satisfied with or dissatisfied with, forming key experience points. When the application data is order data, summarize the products with the most orders in the order data to form key experience points, and obtain the preset key experience points corresponding to the preset service scenario.

[0132] A user experience database is established based on the preset service scenarios and preset key experience points of each preset user.

[0133] Taking the food service scenario as an example, the user holds a terminal device, which also includes an image acquisition device to obtain the user's real-time data. The real-time data includes: current time data, video data captured by the user's terminal with the user's authorization, audio data, and location data. Based on the facial image captured by the image acquisition device, the user's face is recognized to identify the user.

[0134] Based on the time data, video data, audio data and location data obtained with user authorization in the real-time data, for example, when the current time is close to the dining time, the video data includes dishes, drinks or service personnel, the audio data includes ambient sounds and noisy voices, and when the location data is located in a shopping mall or hotel, the service scenario where the user is located is identified as a food service scenario. At the same time, the user's terminal device continuously obtains real-time data.

[0135] In a pre-established user experience database, user matching and service scenario matching are performed separately to obtain key experience points corresponding to users and service scenarios. For example, the matched key experience points are "restaurant", "taste", "environment", "Sichuan cuisine", "parking lot", "internet celebrity", etc. When no user is matched, a new user profile is created.

[0136] Based on real-time data, identify the user's current key experience points and determine the target key experience points that the user has not yet experienced, according to the experience time sequence of each key experience point. For example, based on continuously acquired real-time data, if it is determined that the user is already seated in the restaurant, then "parking lot" is determined as the key experience point that the user has experienced. Combined with the sum of the number of positive and negative reviews of users for historical key experience points in historical experience data, for example, some users value taste and have a high tolerance for the environment, while others value the environment and have average requirements for taste, to assess the user's attention to each target key experience point.

[0137] Based on the level of attention, the key experience points of each target are ranked from highest to lowest to determine the service recommendation scheme for users in each service scenario.

[0138] It should be understood that although the steps in the flowchart above are shown sequentially as indicated by the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless otherwise explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowchart above may include multiple steps or stages, which are not necessarily completed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the steps or stages of other steps.

[0139] In one embodiment, such as Figure 4 As shown, a service recommendation scheme determination device is provided, including: an acquisition module 410, an identification module 420, a matching module 430, an evaluation module 440, and a recommendation module 450, wherein:

[0140] The acquisition module 410 is used to acquire real-time data from users.

[0141] The identification module 420 is used to identify the service scenario in which the user is located based on the real-time data.

[0142] The matching module 430 is used to match key experience points corresponding to the user and the service scenario in a pre-established user experience database. The user experience database stores the preset service scenarios where each preset user is located and the preset key experience points corresponding to each preset service scenario.

[0143] The evaluation module 440 is used to evaluate the user's attention to each target key experience point, and each key experience point includes each target key experience point.

[0144] The recommendation module 450 is used to determine the service recommendation scheme for the user based on the attention level of each service scenario.

[0145] In one embodiment, the identification module 420 includes the following units:

[0146] The data determination unit is used to determine that the real-time data includes at least two of the following: time data, video data authorized by the user, audio data, and location data.

[0147] The recognition unit is used to perform visual recognition based on the video data, and to perceive the service scenario in which the user is located based on the time data, the audio data, and the location data.

[0148] In one embodiment, the matching module 430 includes the following units:

[0149] The user matching unit is used to perform user matching in the user experience database.

[0150] The first user matching unit is used to match the target service scenario corresponding to the user in the user experience database when the user is matched, and to determine each preset key experience point corresponding to the target service scenario as the key experience point.

[0151] The second user matching unit is used to determine similar users to the user in the user experience database based on the real-time data when no user is matched, match the target service scenario corresponding to the similar user in the user experience database, and determine each preset key experience point corresponding to the target service scenario as the key experience point.

[0152] In one embodiment, the first user matching unit includes the following units:

[0153] The scenario matching unit is used to match the user with the preset service scenarios in the user experience database when the user is matched.

[0154] The first scenario matching unit is used to, when a service scenario is matched, take the matched service scenario as the target service scenario and determine each preset key experience point corresponding to the service scenario as the key experience point.

[0155] The second scenario matching unit is used to determine, in the preset service scenarios in the user experience database, a similar service scenario that is similar to the service scenario when no service scenario is matched, and to take the similar service scenario as the target service scenario, and to determine the preset key experience points corresponding to the similar service scenario as the key experience points.

[0156] In one embodiment, the matching module 430 includes the following units:

[0157] The first acquisition unit is used to acquire the preset service scenarios of each preset user.

[0158] The first determining unit is used to obtain preset key experience points corresponding to the preset service scenario based on a preset knowledge graph and scene tags corresponding to the preset service scenario. The knowledge graph stores scene tags and key experience points, and there is an association between the scene tags and the key experience points.

[0159] The first database establishment unit is used to establish a user experience database based on the preset service scenarios of each preset user and the preset key experience points.

[0160] The second acquisition unit is used to acquire application data of each preset user in a preset service scenario. The application data includes at least one of the search data and comment data of each preset user.

[0161] The second determining unit is used to analyze the data of each application, extract the key experience points corresponding to each preset user, and obtain the preset key experience points.

[0162] The second database establishment unit is used to establish a user experience database based on the preset service scenarios and preset key experience points of each preset user.

[0163] In one embodiment, the evaluation module 440 includes the following units:

[0164] The current key experience point identification unit is used to identify the current key experience point corresponding to the user based on the real-time data, wherein each key experience point includes the current key experience point.

[0165] The key experience point sorting unit is used to sort the key experience points to obtain sorted key experience points.

[0166] The target key experience point determination unit is used to determine the current key experience point among the sorted key experience points, and to take each key experience point after the current key experience point as the target key experience point.

[0167] The historical experience data acquisition unit is used to acquire the historical experience data of the user when the user is matched, and otherwise acquire the historical experience data of similar users.

[0168] The historical attention determination unit is used to determine each historical key experience point corresponding to the historical experience data, and to calculate the historical attention corresponding to each historical key experience point.

[0169] The attention evaluation unit is used to evaluate the user's attention to each target key experience point based on the historical attention.

[0170] In one embodiment, the recommendation module 450 includes the following units:

[0171] The target key experience point sorting unit is used to sort the target key experience points according to their respective attention levels, so as to obtain the sorted target key experience points.

[0172] The service recommendation scheme determination unit is used to generate a service recommendation scheme for the user based on the sorted target key experience points.

[0173] In one embodiment, the service recommendation scheme determination apparatus further includes:

[0174] The service effectiveness evaluation unit is used to evaluate the service effectiveness of the service recommendation scheme based on the experience duration of the service recommendation scheme or the user's experience feedback.

[0175] Specific limitations regarding the service recommendation scheme determination device can be found in the limitations of the service recommendation scheme determination method described above, and will not be repeated here. Each module in the aforementioned service recommendation scheme determination device can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device in hardware form, or stored in the memory of a computer device in software form, so that the processor can call and execute the operations corresponding to each module.

[0176] In one embodiment, a computer device is provided, which may be a server, and its internal structure diagram may be as follows. Figure 5As shown, the computer device includes a processor, memory, and a network interface connected via a system bus. The processor provides computing and control capabilities. The memory includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores an operating system, computer programs, and a database. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage medium. The database stores service recommendation scheme determination data. The network interface communicates with external terminals via a network connection. When executed by the processor, the computer program implements a service recommendation scheme determination method.

[0177] In one embodiment, a computer device is provided, which may be a terminal, and its internal structure diagram may be as follows: Figure 6 As shown. The computer device includes a processor, memory, communication interface, display screen, and input devices connected via a system bus. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The communication interface is used for wired or wireless communication with external terminals; wireless communication can be achieved through Wi-Fi, carrier networks, NFC (Near Field Communication), or other technologies. When executed by the processor, the computer program implements a service recommendation scheme determination method. The display screen can be an LCD screen or an e-ink screen. The input devices can be a touch layer covering the display screen, buttons, a trackball, or a touchpad mounted on the computer device casing, or an external keyboard, touchpad, or mouse.

[0178] Those skilled in the art will understand that Figure 5 and Figure 6 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0179] In one embodiment, a computer device is provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps of the service recommendation scheme determination method described above.

[0180] In one embodiment, a computer-readable storage medium is provided having a computer program stored thereon, which, when executed by a processor, implements the steps of the service recommendation scheme determination method described above.

[0181] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the methods described above. Any references to memory, storage, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, or optical storage, etc. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM), etc.

[0182] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0183] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the invention patent. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this patent application should be determined by the appended claims.

Claims

1. A method for determining a service recommendation scheme, the method comprising: obtaining real-time data of a user; the real-time data comprising at least two of time data, video data, audio data and location data authorized to be obtained by the user; performing visual recognition according to the video data, and perceiving a service scenario in which the user is located based on the time data, the audio data and the location data; matching each key experience point corresponding to the user and the service scenario in a pre-established user experience database, the user experience database storing each preset service scenario in which a preset user is located and each preset key experience point corresponding to each preset service scenario; a key experience point is a main demand and a content of focus of a user in a service scenario, and the key experience point comprises at least one of sensory experience, interactive experience and emotional experience; identifying a current key experience point corresponding to the user according to the real-time data, and determining a target key experience point in which the user has not experienced in each key experience point in an experience time sequence of each key experience point; obtaining historical experience data of the user or a similar user, determining each historical key experience point corresponding to the historical experience data, and evaluating a degree of attention of the user to each target key experience point based on a historical degree of attention of each historical key experience point corresponding to each historical key experience point; based on each degree of attention, sorting each target key experience point in a descending order of the degree of attention, and determining a service recommendation scheme of the service scenario to the user.

2. The service recommendation scheme determination method according to claim 1, characterized by, the matching each key experience point corresponding to the user and the service scenario in the pre-established user experience database comprises: matching the user in the user experience database; when the user is matched, matching a target service scenario corresponding to the user in the user experience database, and determining each preset key experience point corresponding to the target service scenario as each key experience point; when the user is not matched, determining a similar user similar to the user in the user experience database according to the real-time data, matching a target service scenario corresponding to the similar user in the user experience database, and determining each preset key experience point corresponding to the target service scenario as each key experience point.

3. The service recommendation plan determination method according to claim 2, characterized by, the matching the target service scenario corresponding to the user in the user experience database when the user is matched, and determining each preset key experience point corresponding to the target service scenario as each key experience point comprises: when the user is matched, matching the service scenario in each preset service scenario in which the user is located in the user experience database; when the service scenario is matched, matching the service scenario as the target service scenario, and determining each preset key experience point corresponding to the service scenario as each key experience point; when the service scenario is not matched, determining a similar service scenario similar to the service scenario in each preset service scenario in the user experience database, matching the similar service scenario as the target service scenario, and determining each preset key experience point corresponding to the similar service scenario as each key experience point.

4. The service recommendation plan determination method according to claim 1, characterized by, The user experience database is established in the following manner, including at least one of the following: The first item: Obtain the preset service scene of each preset user; According to the preset knowledge graph and the scene label corresponding to the preset service scene, obtain the preset key experience point corresponding to the preset service scene, the knowledge graph stores scene labels and key experience points, and there is an association between the scene labels and the key experience points; Based on the preset service scene of each preset user and the preset key experience point, a user experience database is established; The second item: Obtain the application data of each preset user in the preset service scene, the application data including at least one of the search data and the comment data of each preset user; Analyze each application data to extract the key experience point corresponding to each preset user, and obtain the preset key experience point; According to the preset service scene of each preset user and the preset key experience point, a user experience database is established.

5. The service recommendation scenario determination method of claim 1, wherein, After the service recommendation scheme for the user in the service scene is determined by sorting each target key experience point according to the attention degree from high to low based on the attention degree, the method further includes: According to the experience time length of the service recommendation scheme or the experience feedback of the user, the service effect of the service recommendation scheme is evaluated.

6. A service recommendation scheme determination apparatus characterized by comprising: The device includes: An acquisition module for acquiring real-time data of a user, the real-time data including at least two of time data, video data, audio data and location data authorized by the user; An identification module for visual identification based on the video data, and sensing the service scene where the user is based on the time data, the audio data and the location data; A matching module for matching each key experience point corresponding to the user and the service scene in a pre-established user experience database, the user experience database storing the preset service scene of each preset user and each preset key experience point corresponding to each preset service scene; the key experience point is the main demand and key attention of the user in the service scene, and the key experience point includes at least one of sensory experience, interactive experience and emotional experience; An evaluation module for identifying the current key experience point corresponding to the user according to the real-time data, determining the target key experience point which has not been experienced by the user among each key experience point according to the experience time sequence of each key experience point, obtaining the historical experience data of the user or similar users, determining each historical key experience point corresponding to the historical experience data, and evaluating the attention degree of the user to each target key experience point based on the historical attention degree of each historical key experience point; A recommendation module for sorting each target key experience point according to the attention degree from high to low based on the attention degree, and determining the service recommendation scheme for the user in the service scene.

7. The apparatus of claim 6, wherein, The matching module is further configured to: match the user in the user experience database; when the user is matched, match a target service scenario corresponding to the user in the user experience database, and determine each preset key experience point corresponding to the target service scenario as each key experience point; when the user is not matched, determine a similar user similar to the user in the user experience database according to the real-time data, match a target service scenario corresponding to the similar user in the user experience database, and determine each preset key experience point corresponding to the target service scenario as each key experience point.

8. The apparatus of claim 7, wherein, The matching module is further configured to: when the user is matched, match the service scenario in each preset service scenario in which the user is located in the user experience database; when the service scenario is matched, determine each preset key experience point corresponding to the matched service scenario as each key experience point; and when the service scenario is not matched, determine a similar service scenario similar to the service scenario in each preset service scenario in the user experience database, determine each preset key experience point corresponding to the similar service scenario as each key experience point. 9.A computer device, comprising a memory and a processor, wherein the memory stores a computer program, and the processor implements steps of the method in any one of claims 1 to 5 when executing the computer program. 10.A computer readable storage medium, having a computer program stored thereon, wherein the computer program implements steps of the method in any one of claims 1 to 5 when executed by a processor.

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