Article recommendation method and device

Abstract

The invention discloses an article recommendation method and device, and relates to the technical field of electronic commerce. A specific embodiment of the method comprises the following steps: in response to a condition that a user corresponding to an article browsing request satisfies a preset medical insurance condition, obtaining user address information and article information corresponding to the article browsing request; and based on the user address information and the article information, generating and displaying medical insurance article recommendation information associated with the article information. According to the embodiment, when the user meets the preset medical insurance condition, the corresponding medical insurance article can be recommended and guided to the user, so that the user can use the medical insurance account for payment, the actual payment proportion of the user is greatly reduced, and the user experience is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of e-commerce, and in particular to an item recommendation method and device. Background Art

[0002] Health instant retail refers to a scenario where users can purchase medicines, health products, etc. on e-commerce platforms, and the delivery personnel will perform the fulfillment and delivery. In the current health instant retail scenario, the information on the item details page is incomplete, and users can only view the item price and item details, lacking diverse recommended information for users to purchase medicines or health products. Summary of the invention

[0003] In view of this, an embodiment of the present invention provides an item recommendation method and device, which can recommend corresponding medical insurance items to users when the users meet the preset medical insurance conditions, so that users can use their medical insurance accounts to pay, greatly reducing the actual payment ratio of users, thereby improving user experience.

[0004] To achieve the above objective, according to one aspect of an embodiment of the present invention, a method for recommending items is provided, comprising:

[0005] Determine whether the user corresponding to the item browsing request meets the preset medical insurance conditions, and in response to the user meeting the preset medical insurance conditions, obtain the user address information and item information corresponding to the item browsing request;

[0006] Based on user address information and item information, generate and display medical insurance item recommendation information associated with the item information.

[0007] Optionally, the user corresponding to the item browsing request meets preset medical insurance conditions, including:

[0008] Determine whether the user has a historical medical insurance record in the current region based on the acquired user historical order information; or, query the medical insurance interface service based on the acquired user identity information, and determine whether the user has medical insurance qualifications based on the feedback results of the medical insurance interface service.

[0009] Optionally, based on the user address information and the item information, medical insurance item recommendation information associated with the item information is generated and displayed, including:

[0010] Determine a target merchant set and a target item set based on the user address information and item information; wherein the target items are the same medical insurance items, similar medical insurance items, or matching medical insurance items;

[0011] The recommendation coefficient of each target item is determined according to the target merchant set and the target item set, and the medical insurance item recommendation information is generated and displayed based on the recommendation coefficient of each target item.

[0012] Optionally, determining a target merchant set and a target item set based on the user address information and the item information includes:

[0013] Parse the user's address information to obtain longitude and latitude coordinates, and determine the first candidate merchant set based on the longitude and latitude coordinates;

[0014] Determine a candidate item set corresponding to the item based on the item information and a preset logical scenario, and query a second candidate merchant set corresponding to the candidate item set;

[0015] The intersection of the first candidate merchant set and the second candidate merchant set is taken, and the target merchant set and the target item set are determined based on the intersection result.

[0016] Optionally, determining a candidate item set corresponding to the item based on the item information and a preset logical scenario includes:

[0017] Search for identical medical insurance items that fully match the item based on the item information;

[0018] In response to finding the same medical insurance item, generating a candidate item set based on the same medical insurance item;

[0019] In response to not finding the same medical insurance item, similar medical insurance items that partially match the item are searched based on the item information and a preset similarity rule, and a candidate item set is generated based on the similar medical insurance items.

[0020] Optionally, the method further comprises:

[0021] In response to no similar medical insurance items being found, medical insurance items having a matching relationship with the items are searched based on the item information and a preset item matching relationship to generate a candidate item set.

[0022] Optionally, determining a recommendation coefficient for each target item according to the target merchant set and the target item set includes:

[0023] Obtaining the timeliness information of each target merchant in the target merchant set and the price information of each target item in the target item set;

[0024] Normalize the timeliness information of each target merchant and the price information of each target item;

[0025] The normalized timeliness information of each target merchant and the price information of each target item are weighted and summed, and the recommendation coefficient of each target item is determined based on the calculation result.

[0026] Optionally, based on the recommendation coefficient of each target item, medical insurance item recommendation information is generated and displayed, including:

[0027] Sort the recommendation coefficient of each target item, and generate and display medical insurance item recommendation information based on the sorting results.

[0028] According to a second aspect of an embodiment of the present invention, there is provided an item recommendation device, comprising:

[0029] An acquisition module, in response to the user corresponding to the item browsing request meeting a preset medical insurance condition, acquires user address information and item information corresponding to the item browsing request;

[0030] The recommendation module generates and displays the medical insurance item recommendation information associated with the item information based on the user's address information and item information.

[0031] According to a third aspect of an embodiment of the present invention, there is provided an electronic device, including:

[0032] one or more processors;

[0033] a memory for storing one or more programs,

[0034] When one or more programs are executed by one or more processors, the one or more processors are enabled to implement the method of any of the above embodiments.

[0035] According to a fourth aspect of an embodiment of the present invention, a computer-readable medium is provided, on which a computer program is stored, and when the program is executed by a processor, the method of any of the above embodiments is implemented.

[0036] According to a fifth aspect of an embodiment of the present invention, there is provided a computer program product, including a computer program, which implements the method of any of the above embodiments when the computer program is executed by a processor.

[0037] One embodiment of the above invention has the following advantages or beneficial effects: by responding to the user corresponding to the item browsing request meeting the preset medical insurance conditions, the user address information and item information corresponding to the item browsing request are obtained; based on the user address information and item information, medical insurance item recommendation information associated with the item information is generated and displayed; thereby, when the user meets the preset medical insurance conditions, the corresponding medical insurance items can be recommended to the user, so that the user can use the medical insurance account for payment, which greatly reduces the actual payment ratio of the user, thereby improving the user experience.

[0038] The further effects of the above-mentioned non-conventional optional manner will be described below in conjunction with the specific implementation manner. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] The accompanying drawings are used to better understand the present invention and do not constitute an improper limitation of the present invention.

[0040] Figure 1 is a schematic diagram of the main process of the item recommendation method according to an embodiment of the present invention;

[0041] Figure 2is a schematic diagram of the main process of an item recommendation method according to an optional embodiment of the present invention;

[0042] Figure 3 is a schematic diagram of main modules of an item recommendation device according to an embodiment of the present invention;

[0043] Figure 4 is an exemplary system architecture diagram to which embodiments of the present invention may be applied;

[0044] Figure 5 It is a schematic diagram of the structure of a computer system of a terminal device or a server suitable for implementing an embodiment of the present invention. DETAILED DESCRIPTION

[0045] The following is a description of exemplary embodiments of the present invention in conjunction with the accompanying drawings, including various details of the embodiments of the present invention to facilitate understanding, which should be considered as merely exemplary. Therefore, it should be recognized by those of ordinary skill in the art that various changes and modifications may be made to the embodiments described herein without departing from the scope and spirit of the present invention. Similarly, for clarity and conciseness, the description of well-known functions and structures is omitted in the following description.

[0046] It should be noted that the acquisition, storage and application of personal information etc. involved in the embodiments of the present invention are in compliance with the provisions of relevant laws and regulations and do not violate public order and good morals.

[0047] Health instant retail refers to a scenario where users can purchase medicines, health products, etc. on e-commerce platforms, and the delivery personnel will perform the fulfillment and delivery. In the current health instant retail scenario, the information on the item details page is incomplete, and users can only view the item price and item details, lacking diverse recommended information for users to purchase medicines or health products.

[0048] In view of this, according to one aspect of an embodiment of the present invention, a method for recommending an item is provided.

[0049] Figure 1 FIG. 1 is a schematic diagram of the main process of the item recommendation method according to an embodiment of the present invention. Figure 1 As shown, the item recommendation method according to the embodiment of the present invention includes the following steps S101 to S102.

[0050] Step S101, in response to the user corresponding to the item browsing request satisfying a preset medical insurance condition, obtaining user address information and item information corresponding to the item browsing request.

[0051] Item browsing requests refer to interactive behaviors initiated by users on the platform through specific operations (such as clicking on the product details page or searching for products) to obtain detailed information about specific items. Users include individuals who browse and purchase products through the platform. Preset medical insurance conditions are rules set by the platform based on business needs to determine whether users meet specific medical insurance qualifications, such as whether the user has a historical medical insurance record in the current region, or whether the user's identity is authenticated as eligible for medical insurance in the service interface of the Medical Insurance Bureau, etc. The user address information corresponding to the item browsing request refers to the delivery address or current location recorded by the system or actively provided by the user when the user browses the product, usually including detailed address, city area, postal code, etc.; item information refers to the specific attributes of the product requested by the user to view, including SKU (stock keeping unit) number, product name, category, price, inventory status, etc.

[0052] To determine whether the user meets the preset medical insurance conditions, you can query based on the user's historical order information and search the order system to see if there is a historical record of the user meeting the medical insurance conditions in the current region; you can also call the interface service of the Medical Insurance Bureau through the user's identity information to verify in real time whether the user has medical insurance qualifications. After confirming that the user meets the preset medical insurance conditions, directly extract the address information and item information from the item browsing request, such as parsing the address field and product SKU from the request parameters; you can also retrieve the user's default delivery address from the database through the user ID (identity identifier) ​​or session ID, and query the product database in combination with the requested product ID to obtain detailed information on the item, so that the response to the item browsing request can quickly and efficiently obtain accurate data for subsequent logical processing.

[0053] Step S102: Generate and display medical insurance item recommendation information associated with the item information based on the user address information and the item information.

[0054] The medical insurance item recommendation information associated with the item information is a recommended list of the same, similar or matching medical insurance items and related information generated by combining the items queried by the user and the medical insurance policy. This recommended information includes the name, price, available medical insurance quota, merchant location, delivery time, etc. of the medical insurance item.

[0055] Specifically, the medical insurance item recommendation information is generated based on the medical insurance catalog and item similarity rules based on the user's location. First, the local medical insurance catalog is queried using the user's address information to screen out medical insurance items that fully match the current item information. If there is no fully matching item, the medical insurance item that is closest to the query item in terms of attributes is found based on the similarity rules, such as by comparing key attributes (such as product category, functional use, etc.). The generated recommendation information is integrated with the inventory and delivery information of the corresponding merchant, and displayed in the form of a card-style list through the user interface. Each card includes detailed information such as item name, price, merchant information, and timeliness. In addition, the recommendation information can also be generated through item matching relationships. Based on the item information and the preset matching rules, the common matching items of the current item in the medical insurance catalog are queried, such as auxiliary treatment drugs, related medical devices, etc. These matching relationships can be determined by historical sales data mining or manually configured logical rules. The best merchant is selected based on the merchant coverage of the matching items and the user's address information, and the recommendation information is generated in combination with the price and delivery information. Recommendation information can be presented in the form of "combination recommendations". Users can see the recommended product combinations and their total prices, medical insurance reimbursement ratios, etc. They can also choose to purchase the entire set of recommended items with one click to improve the efficiency of medical insurance item recommendations and user experience.

[0056] The embodiment of the present invention obtains the user address information and item information corresponding to the item browsing request in response to the user corresponding to the item browsing request satisfying the preset medical insurance conditions; generates and displays the medical insurance item recommendation information associated with the item information based on the user address information and the item information; thereby, when the user meets the preset medical insurance conditions, the corresponding medical insurance items can be recommended to the user, so that the user can use the medical insurance account to pay, which greatly reduces the actual payment ratio of the user, thereby improving the user experience.

[0057] Optionally, the user corresponding to the item browsing request meets preset medical insurance conditions, including:

[0058] Determine whether the user has a historical medical insurance record in the current region based on the acquired user historical order information; or, query the medical insurance interface service based on the acquired user identity information, and determine whether the user has medical insurance qualifications based on the feedback results of the medical insurance interface service.

[0059] When determining based on the user's historical order information, you can query the user's order record to check whether the user has completed medical insurance-related orders in the current region. If there is a historical order and the record shows that the user used the medical insurance payment method, it can be determined that the user has a medical insurance record in the current region, thereby meeting the preset medical insurance conditions; determination based on user behavior data is real-time and efficient. In addition, you can also query the medical insurance interface service through the user's identity information, use the user's identity information (such as ID card number or medical insurance card number) to initiate a query request to the interface of the medical insurance bureau or relevant institutions, and determine whether the user has medical insurance qualifications based on the feedback results of the interface. If the interface returns a valid medical insurance authentication result, for example, the user has been registered in the local medical insurance system, then the user is deemed to meet the preset medical insurance conditions. This method relies on authoritative data sources to improve the accuracy and legitimacy of the judgment results.

[0060] In addition, a geographic verification method based on the user's real-time location and medical insurance coverage can also be used. The user's current location information is obtained through GPS or IP address resolution, and the medical insurance coverage corresponding to the location is matched. For example, query the geographic service interface of the medical insurance system to confirm whether the user's location is within the medical insurance service scope. If the user's location matches the medical insurance service area and other information is verified correctly, it can be determined that the user meets the preset medical insurance conditions. This method supplements the verification logic of the geographic dimension and is suitable for cross-regional users.

[0061] The embodiment of the present invention combines user historical data, authoritative interface query and geographic information verification to determine whether the user meets the preset medical insurance conditions in a multi-level and multi-dimensional manner, thereby improving the comprehensiveness and flexibility of the judgment, thereby providing a reliable basis for the subsequent item recommendation process.

[0062] Optionally, Figure 2 is a schematic diagram of the main process of the item recommendation method according to an optional embodiment of the present invention; Figure 2 As shown, based on the user address information and the item information, the medical insurance item recommendation information associated with the item information is generated and displayed, including the following steps S201 and S202.

[0063] Step S201, determining a target merchant set and a target item set based on user address information and item information; wherein the target items are the same medical insurance items, similar medical insurance items, or matching medical insurance items.

[0064] Step S202, determining a recommendation coefficient for each target item according to the target merchant set and the target item set, and generating and displaying medical insurance item recommendation information based on the recommendation coefficient for each target item.

[0065] In the above step S201, the target merchant set refers to a set of merchants that can provide the user's requested items or substitute item services within the user's current address range. The target item set is a set of medical insurance products related to the items browsed by the user, including products that can directly meet the user's needs (same medical insurance items), products with similar attributes (similar medical insurance items), and matching products recommended to the user (matching medical insurance items). The same medical insurance item can be a medical insurance product that is consistent with the item browsed by the user, for example, a product with a consistent 69-code unique identifier. Similar medical insurance items refer to products that have similar core attributes to the items browsed by the user, such as brand, generic name, specification, manufacturer, etc. Matching medical insurance items refer to products that have a predefined matching relationship with the items browsed by the user. For example, through the "main product + accessory" pair maintained by the database, a set of medical insurance products that can be used in combination is matched to provide users with value-added product recommendation services.

[0066] To determine the target merchant set and target item set, you can parse the longitude and latitude coordinates through the user address information, call the merchant service interface to obtain all nearby merchant sets; then combine the merchant product inventory data to filter out merchants that contain relevant medical insurance items as the target merchant set. You can also directly retrieve the merchant list in the current area from the database based on the user address information, and then filter out the target merchants and target item sets that meet the conditions based on the item SKU or its related attributes. In the above way, you can dynamically combine the user's location and needs to generate the target merchant set and target item set.

[0067] In the above step S202, the recommendation coefficient is a comprehensive indicator used to measure the priority recommendation degree of the target item, which can be calculated based on the relevant characteristics of the target merchant and the user value of the item, and usually includes factors affecting user decision-making such as the product price, delivery time, and merchant reputation.

[0068] Specifically, the recommendation coefficient can be determined based on the normalized evaluation method, the delivery time and the price of each target merchant and target item can be standardized, and the recommendation coefficient can be calculated through a weighted formula; it can ensure the fairness of the recommendation results and highlight the core indicators that users care about. It is also possible to predict the user's preference score for the target item through a machine learning model, train the model based on historical user behavior, product attributes and merchant characteristics, and directly output the recommendation score as the recommendation coefficient. After obtaining the recommendation coefficient of each target item, the recommended items are directly displayed after being sorted based on the recommendation coefficient. For example, for scenario 1 (same medical insurance items) and scenario 2 (similar medical insurance items), the best merchant products are selected and displayed in order from low to high in the recommendation coefficient to ensure that the products seen by users have the highest cost performance. For scenario 3 (matching medical insurance items), the top 10 merchant product sets are selected according to the recommendation coefficient, and the combination is recommended to the user. In addition, the recommendation algorithm can also be combined with the recommendation coefficient to dynamically adjust the recommended content. For example, collaborative filtering or content-based recommendation methods are used to add user historical preferences, purchase behavior and other characteristics on the basis of the recommendation coefficient to generate more personalized recommendation results, and a diversified product recommendation list is displayed through the user interface. By combining the characteristics of the target merchant set and the target item set, using the recommendation coefficient to measure the priority of the items, and adopting different recommendation strategies, the user's choice satisfaction and the accuracy of the recommendation system can be improved.

[0069] Optionally, determining a target merchant set and a target item set based on user address information and item information includes: parsing the user address information to obtain longitude and latitude coordinates, and determining a first candidate merchant set based on the longitude and latitude coordinates; determining a candidate item set corresponding to the item based on the item information and a preset logical scenario, and querying a second candidate merchant set corresponding to the candidate item set; taking the intersection of the first candidate merchant set and the second candidate merchant set, and determining a target merchant set and a target item set based on the intersection result.

[0070] First, parse the user's address information and convert the address information into specific longitude and latitude coordinates through the geocoding service. Then, based on these longitude and latitude coordinates, call the merchant positioning service to filter out the first set of candidate merchants within a certain range from the user's location. For example, set the search radius to 5 kilometers, and query the nearby merchant database to obtain a set of merchants that can serve the user. At the same time, according to the preset logical scenarios matched by the item information, such as the same medical insurance items, similar medical insurance items, or matching medical insurance items, the candidate item set is obtained from the item database. For each item in the candidate item set, query its associated merchant information to generate a second candidate merchant set. Finally, take the intersection of the first candidate merchant set and the second candidate merchant set to ensure that the merchants in the intersection result are both within the user's service range and have recommended items, and finally determine the target merchant set and target item set.

[0071] In addition, you can also filter based on the merchant's fulfillment capabilities and item matching relationships. When parsing the user's address information, in addition to obtaining the longitude and latitude coordinates, you can also call the merchant service capability interface to accurately filter the first candidate merchant set based on the delivery time and service area provided by the merchant. For example, give priority to merchants with short delivery times or specific medical insurance qualifications. Use item information to match merchant inventory data, combine item 69 code or key attributes (such as generic name, specifications, manufacturer) to find the corresponding candidate item set, and determine the second candidate merchant set. Then further integrate the merchant's inventory data, fulfillment capabilities, and the importance weight of user needs, take the intersection of the first candidate merchant set and the second candidate merchant set, and evaluate the recommendation priority of each merchant in the intersection to optimize the recommendation results of the target merchant set and target item set.

[0072] The embodiment of the present invention combines user address information and item information to gradually screen and optimize the merchant and item set, which not only enables merchants to serve users, but also ensures that the recommended items have high relevance and satisfaction, thereby providing users with more accurate recommendation services.

[0073] Optionally, determining a candidate item set corresponding to the item based on the item information and a preset logical scenario includes: querying identical medical insurance items that completely match the item based on the item information; in response to finding the identical medical insurance items, generating a candidate item set based on the identical medical insurance items; in response to not finding the identical medical insurance items, querying similar medical insurance items that partially match the item based on the item information and preset similarity rules, and generating a candidate item set based on the similar medical insurance items.

[0074] In the process of determining the candidate item set based on item information and preset logical scenarios, different query logics and matching rules can be used to generate a set of items that meet the conditions. Specifically, to directly match the same medical insurance items, first use the item information (such as product number, medical insurance classification code or product common name, etc.) as the query condition, perform accurate matching in the medical insurance item database, and find completely identical medical insurance items. For example, for medicines with clear medical insurance numbers, all items with consistent attributes in the database can be quickly retrieved and added to the candidate item set. If multiple completely identical items are matched, the candidate item set can be further optimized through screening rules (such as the latest production date, inventory or the merchant closest to the user) to ensure the accuracy and timeliness of the recommendation results. When the same medical insurance item cannot be found, find items that have partial feature matching with the target item based on the preset similarity rules. The similarity rules can include multiple dimensions, such as common name similarity, functional category consistency, same manufacturer or similar specifications. By calculating the feature vector of the item information, the target item is compared with other items in the database, and items with similarity higher than the preset threshold are screened out and added to the candidate item set as similar medical insurance items. For example, if a user searches for a cold medicine, but there is no exact match in the database, a candidate item set can be generated by matching other cold medicines with similar generic names or the same action categories. In addition, similar items can be sorted and optimized based on user needs (such as price, brand preference, or inventory status) to further improve the recommendation effect.

[0075] The embodiments of the present invention can effectively cope with different item matching scenarios, and can generate a candidate item set that meets user needs regardless of exact matching or fuzzy matching, thereby improving the reliability and diversity of recommendation results.

[0076] Optionally, the method further includes: in response to no similar medical insurance items being found, searching for medical insurance items that have a matching relationship with the item based on the item information and a preset item matching relationship, and generating a candidate item set.

[0077] In the case that no similar medical insurance items are found, a candidate item set is generated through a preset item collocation relationship, which can improve the diversity of recommendations and user satisfaction. Based on the fixed collocation rules predefined in the database, medical insurance items that have a collocation relationship with the target item are queried. For example, the system pre-stores the commonly used combination treatment collocation information between drugs, such as the collocation of cold medicines with vitamin C, the combination of antihypertensive drugs and diuretics, etc. When a user browses a target item, the relevant medical insurance items can be queried according to the collocation rules, and these collocation items can be included in the candidate item set. For example, when a user browses a cold medicine but does not find a similar medicine, a vitamin C can be recommended according to the collocation rules to generate a collocation candidate item set. This method is suitable for medical scenarios with clear collocation relationships and can provide highly practical recommendation results. In addition, data mining technology can also be used to dynamically mine the potential collocation relationship of items based on user historical behavior data and purchasing habits. For example, by analyzing the purchase records of a large number of users, it is possible to find a high-frequency co-occurrence relationship between certain items, such as anti-inflammatory drugs and stomach medicines are often purchased at the same time. When a user browses an item but fails to find similar items, the mined matching relationships can be used to query other items that are highly correlated with the target item and add them to the candidate item set. For example, when a user browses an antibiotic, a stomach medicine or probiotics can be recommended as a matching item. This method is suitable for mining potential needs and personalized recommendations, and can dynamically adjust matching relationships to improve the flexibility of recommendations.

[0078] The embodiments of the present invention are based on rule matching and data-driven, and can cover different user scenarios. In medical scenarios with clear rules, fixed matching rules can provide accurate recommendations; while in the case of diversified user behaviors, data mining technology can explore potential needs, thereby improving the personalization and practicality of recommendations.

[0079] Optionally, the recommendation coefficient of each target item is determined based on the target merchant set and the target item set, including: obtaining the timeliness information of each target merchant in the target merchant set and the price information of each target item in the target item set; normalizing the timeliness information of each target merchant and the price information of each target item; performing weighted sum calculation on the normalized timeliness information of each target merchant and the price information of each target item, and determining the recommendation coefficient of each target item based on the calculation result.

[0080] Based on the target merchant set and the target item set, the recommendation coefficient of each target item is determined. By comprehensively considering the merchant's timeliness information and the item's price information in a weighted manner, a recommendation that better meets the user's needs can be achieved. Specifically, based on the fixed weight configuration method, fixed weight parameters can be preset in the system to assign different weights to timeliness information and price information. For example, if timeliness is more important to the user's purchase decision, the weight of timeliness information can be set to 0.7, and the weight of price information can be set to 0.3. First, the timeliness information of the target merchant and the price information of the target item are normalized to the [0, 1] interval; after normalization, the timeliness information and price information of each target item are weighted and summed to obtain the recommendation coefficient; the target items are sorted from high to low or from low to high according to the recommendation coefficient, and the top items are recommended to the user. In addition, the weights of timeliness information and price information can also be dynamically adjusted according to user behavior characteristics based on the dynamic weight adjustment method. For example, by analyzing the user's historical order data, if it is found that the user is more inclined to choose products with shorter delivery time in emergency situations (such as promotional activities), the weight of time information can be increased; in non-emergency situations, it is more inclined to reduce the weight of time and increase the weight of price. First, the time information of the target merchant and the price information of the target item are normalized, and the recommendation coefficient is calculated based on the dynamically adjusted weight. The weight value is driven by user behavior data. Through dynamic weight adjustment, personalized recommendations can be achieved to better meet the actual needs of users.

[0081] The fixed weight method in the embodiment of the present invention is suitable for making recommendations in general scenarios, while the dynamic weight adjustment method is suitable for scenarios where user needs are diverse or change frequently, and can improve the flexibility and accuracy of the recommendation system.

[0082] Optionally, generating and displaying the medical insurance item recommendation information based on the recommendation coefficient of each target item includes: sorting the recommendation coefficient of each target item, and generating and displaying the medical insurance item recommendation information based on the sorting result.

[0083] When recommending items to users based on the recommendation coefficients of target items, the recommendation coefficients of all target items can be globally sorted based on the global sorting recommendation method, and they can be arranged in order from high to low or from low to high according to the recommendation coefficients, and several items with the highest ranking can be selected for recommendation. In the specific implementation process, the recommendation coefficients of all target items are first obtained and stored in a data structure (such as a list or a priority queue); the data structure is sorted, and the sorting results are generated using efficient algorithms such as quick sort or heap sort; the first few items are extracted according to the sorting results, and the recommended content is displayed through the user interface. In addition, the recommendation method can also be based on group sorting. For the target merchant set and the target item set, the items can be grouped according to the merchant dimension first, and then the recommendation coefficients of each group of items can be sorted separately; in each merchant group, the items are arranged from high to low or from low to high according to the recommendation coefficient, and the first few items in each group are selected as the recommendation candidate set; according to the user's preference or merchant priority, the recommendation candidate set of each merchant is comprehensively sorted again, and the result is finally recommended to the user. For example, when a user prefers certain specific merchants, the priority of the items in these merchants can be increased so that the recommendation results are more in line with the user's preferences.

[0084] According to a preferred embodiment of the present invention, when a user enters the product details page, based on the user's order record, it is queried whether the user has a medical insurance record in the current area, or whether the medical insurance conditions are met by querying through the interface service of the medical insurance bureau. If the medical insurance conditions are met, the SKU information of the product is obtained through the interface service. The latitude and longitude coordinates are parsed according to the user's delivery address through the service interface, and the set A of nearby fulfillable stores is obtained through the coordinate query interface, which is mainly divided into the following three logical scenarios: Scenario 1 recommends the same medical insurance products in nearby stores. The 69-code information of the product is obtained according to the sku. Since the 69-code is maintained in the database before each product is put on the shelf, the medical insurance product database can be queried according to the 69-code match to query the set of the same medical insurance product SKUs. If no product is found, the logic of Scenario 2 is executed; Scenario 2 recommends similar medical insurance products in nearby stores. Before each product is put on the shelf, the basic product information, including attributes (brand, generic name, specification, manufacturer, etc.) is maintained and saved in the medical insurance product database. According to the rule condition of ("generic name + specification + manufacturer" information), the medical insurance product database is queried to obtain the set of similar SKUs. If no product is found, the logic of Scenario 3 is executed; Scenario 3 matches the medical insurance products in nearby stores. Before each product is put on the shelf, a unique drugId (identifier) is generated according to the product attributes (brand + generic name + specification + manufacturer) rule. The drug matching relationship is preset and maintained by the database relationship operation and maintenance personnel, and the data structure is <drugId of the main product + drugId of the accessory product>. By querying the drugId corresponding to the SKU and matching it with the preset matching pair information in the <key, value> structure, as long as the key or value is equal, the set of matching SKUs can be obtained.

[0085] After the above three scenarios, the corresponding store set B is queried through the obtained SKU set, and the store set A is matched with the set B to obtain the target store and target product set that meet the requirements. In scenarios 1, 2, and 3, the basic information, timeliness, target product price, etc. of the target store are obtained through the interface service, and the recommendation coefficient is calculated by normalization. Among them, the following formula is used for standardization first: anew = (X-Xmin) / (Xmax-Xmin), bnew = (X-Xmin) / (Xmax-Xmin); where a is the store timeliness, b is the product price, anew represents the parameter of using timeliness a, and bnew represents the parameter of using the product price. Then the recommendation coefficient I = m×anew + n×bnew (m and n are configuration items) is calculated. After obtaining the recommendation coefficient I, it is sorted from low to high. In scenarios 1 and 2, the products of the store with the smallest score are displayed on the product details page, and in scenario 3, the products of the top 10 stores are selected according to the above rules to be displayed on the product details page. In scenario 1, the user completes the purchase by clicking the Add to Cart button of the recommended medical insurance product; in scenario 2, the user completes the purchase by clicking the Add to Cart button of the recommended similar medical insurance product; in scenario 3, the user clicks the recommended matching medical insurance product, and the user can swipe left and right to select the matching medical insurance card and add it to the cart to complete the purchase.

[0086] In the product details page scenario, the embodiments of the present invention use non-medical insurance products as the basis and recommend medical insurance-related, similar, and matching products from nearby stores based on geographic location information, thereby enhancing timeliness and shaping users' perception of the platform's fast delivery. When recommending medical insurance products, users can use their medical insurance accounts to pay, greatly reducing the user's actual payment ratio and shaping the platform's image of "medical insurance benefits the people."

[0087] According to a second aspect of an embodiment of the present invention, a device for recommending items is provided.

[0088] Figure 3 Schematic diagram of the main modules of the item recommendation device according to an embodiment of the present invention. Figure 3 As shown, the item recommendation device 300 includes:

[0089] Acquisition module 301, in response to the user corresponding to the item browsing request meeting the preset medical insurance condition, acquires the user address information and item information corresponding to the item browsing request;

[0090] The recommendation module 302 generates and displays the medical insurance item recommendation information associated with the item information based on the user address information and the item information.

[0091] Optionally, the item recommendation device 300 further includes a determination module, which is used to:

[0092] Determine whether the user has a historical medical insurance record in the current region based on the acquired user historical order information; or, query the medical insurance interface service based on the acquired user identity information, and determine whether the user has medical insurance qualifications based on the feedback results of the medical insurance interface service.

[0093] Optionally, the recommendation module 302 is further configured to:

[0094] Determine a target merchant set and a target item set based on the user address information and item information; wherein the target items are the same medical insurance items, similar medical insurance items, or matching medical insurance items;

[0095] The recommendation coefficient of each target item is determined according to the target merchant set and the target item set, and the medical insurance item recommendation information is generated and displayed based on the recommendation coefficient of each target item.

[0096] Optionally, the recommendation module 302 is further configured to:

[0097] Parse the user's address information to obtain longitude and latitude coordinates, and determine the first candidate merchant set based on the longitude and latitude coordinates;

[0098] Determine a candidate item set corresponding to the item based on the item information and a preset logical scenario, and query a second candidate merchant set corresponding to the candidate item set;

[0099] The intersection of the first candidate merchant set and the second candidate merchant set is taken, and the target merchant set and the target item set are determined based on the intersection result.

[0100] Optionally, the recommendation module 302 is further configured to:

[0101] Search for identical medical insurance items that fully match the item based on the item information;

[0102] In response to finding the same medical insurance item, generating a candidate item set based on the same medical insurance item;

[0103] In response to not finding the same medical insurance item, similar medical insurance items that partially match the item are searched based on the item information and a preset similarity rule, and a candidate item set is generated based on the similar medical insurance items.

[0104] Optionally, the item recommendation device 300 further includes a matching module, which is used to:

[0105] In response to no similar medical insurance items being found, medical insurance items having a matching relationship with the items are searched based on the item information and a preset item matching relationship to generate a candidate item set.

[0106] Optionally, the recommendation module 303 is further used for:

[0107] Obtaining the timeliness information of each target merchant in the target merchant set and the price information of each target item in the target item set;

[0108] Normalize the timeliness information of each target merchant and the price information of each target item;

[0109] The normalized timeliness information of each target merchant and the price information of each target item are weighted and summed, and the recommendation coefficient of each target item is determined based on the calculation result.

[0110] Optionally, the recommendation module 303 is further used for:

[0111] Sort the recommendation coefficient of each target item, and generate and display medical insurance item recommendation information based on the sorting results.

[0112] It should be noted that the specific implementation content of the item recommendation device of the present invention has been described in detail in the above item recommendation method, so the repeated content will not be described again here.

[0113] According to a third aspect of an embodiment of the present invention, there is provided an electronic device, comprising: one or more processors; a storage device for storing one or more programs, wherein when the one or more programs are executed by the one or more processors, the one or more processors implement the method provided by the first aspect of the embodiment of the present invention.

[0114] According to a fourth aspect of an embodiment of the present invention, a computer-readable medium is provided, on which a computer program is stored, and when the program is executed by a processor, the method provided by the first aspect of the embodiment of the present invention is implemented.

[0115] According to a fifth aspect of an embodiment of the present invention, a computer program product is provided, including a computer program, and when the computer program is executed by a processor, the method provided by the first aspect of the embodiment of the present invention is implemented.

[0116] Figure 4 An exemplary system architecture 400 is shown to which the item recommendation method or item recommendation apparatus according to the embodiments of the present invention can be applied.

[0117] like Figure 4 As shown, system architecture 400 may include terminal devices 401, 402, 403, network 404 and server 405. Network 404 is used to provide a medium for communication links between terminal devices 401, 402, 403 and server 405. Network 404 may include various connection types, such as wired, wireless communication links or optical fiber cables, etc.

[0118] Users can use terminal devices 401, 402, 403 to interact with server 405 through network 404 to receive or send messages, etc. Various communication client applications can be installed on terminal devices 401, 402, 403, such as shopping applications, web browser applications, search applications, instant messaging tools, email clients, social platform software, etc. (only for example).

[0119] The terminal devices 401 , 402 , and 403 may be various electronic devices having a display screen and supporting web browsing, including but not limited to smart phones, tablet computers, laptop computers, and desktop computers.

[0120] The server 405 may be a server that provides various services, such as a backend management server (for example only) that provides support for shopping websites browsed by users using the terminal devices 401, 402, and 403. The backend management server may analyze and process the received data such as item recommendations, and feed back the processing results (such as target items - for example only) to the terminal device.

[0121] It should be noted that the item recommendation method provided in the embodiment of the present invention is generally executed by the server 405 , and accordingly, the item recommendation device is generally disposed in the server 405 .

[0122] It should be understood that Figure 4 The number of terminal devices, networks and servers in the embodiment is only for illustration. Any number of terminal devices, networks and servers may be provided according to implementation requirements.

[0123] Reference below Figure 5 , which shows a schematic diagram of the structure of a computer system 500 of a terminal device suitable for implementing an embodiment of the present invention. Figure 5 The terminal device shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present invention.

[0124] like Figure 5 As shown, the computer system 500 includes a central processing unit (CPU) 501, which can execute various appropriate actions and processes according to a program stored in a read-only memory (ROM) 502 or a program loaded from a storage part 508 into a random access memory (RAM) 503. In the RAM 503, various programs and data required for the operation of the system 500 are also stored. The CPU 501, the ROM 502, and the RAM 503 are connected to each other through a bus 504. An input / output (I / O) interface 505 is also connected to the bus 504.

[0125] The following components are connected to the I / O interface 505: an input section 506 including a keyboard, a mouse, etc.; an output section 507 including a cathode ray tube (CRT), a liquid crystal display (LCD), etc., and a speaker, etc.; a storage section 508 including a hard disk, etc.; and a communication section 509 including a network interface card such as a LAN card, a modem, etc. The communication section 509 runs a communication process via a network such as the Internet. A drive 510 is also connected to the I / O interface 505 as needed. A removable medium 511, such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, etc., is installed on the drive 510 as needed, so that a computer program read therefrom is installed into the storage section 508 as needed.

[0126] In particular, according to the embodiments disclosed in the present invention, the process described above with reference to the flowchart can be implemented as a computer software program. For example, the embodiments disclosed in the present invention include a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program contains program code for running the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from the network through the communication part 509, and / or installed from the removable medium 511. When the computer program is executed by the central processing unit (CPU) 501, the above-mentioned functions defined in the system of the present invention are executed.

[0127] It should be noted that the computer-readable medium shown in the present invention may be a computer-readable signal medium or a computer-readable storage medium or any combination of the above two. The computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In the present invention, a computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in combination with an instruction execution system, device or device. In the present invention, a computer-readable signal medium may include a data signal propagated in a baseband or as part of a carrier wave, which carries a computer-readable program code. This propagated data signal may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. The computer-readable signal medium may also be any computer-readable medium other than a computer-readable storage medium, which may send, propagate or transmit a program for use by or in conjunction with an instruction execution system, apparatus or device. The program code contained on the computer-readable medium may be transmitted using any appropriate medium, including but not limited to: wireless, wire, optical cable, RF, etc., or any suitable combination of the above.

[0128] The flow chart and block diagram in the accompanying drawings illustrate the possible architecture, functions and operations of the system, method and computer program product according to various embodiments of the present invention. In this regard, each box in the flow chart or block diagram can represent a module, a program segment, or a part of a code, and the above-mentioned module, program segment, or a part of a code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order from the order marked in the accompanying drawings. For example, two boxes represented in succession can actually run basically in parallel, and they can sometimes run in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram or flow chart, and the combination of the boxes in the block diagram or flow chart can be implemented with a dedicated hardware-based system that runs the specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0129] The modules involved in the embodiments of the present invention may be implemented in software or hardware. The modules described may also be set in a processor, for example, they may be described as: a processor includes an acquisition module and a recommendation module. The names of these modules do not constitute a limitation on the modules themselves in some cases. For example, the acquisition module may also be described as "a module for obtaining user address information and item information corresponding to an item browsing request in response to the user corresponding to the item browsing request satisfying a preset medical insurance condition."

[0130] As another aspect, the present invention further provides a computer-readable medium, which may be included in the device described in the above embodiment; or may exist independently without being assembled into the device. The above computer-readable medium carries one or more programs, and when the above one or more programs are executed by a device, the device includes: in response to the user corresponding to the item browsing request meeting the preset medical insurance condition, obtaining the user address information and item information corresponding to the item browsing request; based on the user address information and item information, generating and displaying the medical insurance item recommendation information associated with the item information.

[0131] The computer program product provided by the embodiment of the present invention includes a computer program, and when the computer program is executed by a processor, the item recommendation method in the embodiment of the present invention is implemented.

[0132] The technical solution according to the embodiment of the present invention has the following advantages or beneficial effects: by responding to the user corresponding to the item browsing request meeting the preset medical insurance conditions, the user address information and item information corresponding to the item browsing request are obtained; based on the user address information and item information, medical insurance item recommendation information associated with the item information is generated and displayed; thereby, when the user meets the preset medical insurance conditions, the corresponding medical insurance items can be recommended to the user, so that the user can use the medical insurance account to pay, which greatly reduces the actual payment ratio of the user, thereby improving the user experience.

[0133] The above specific implementations do not constitute a limitation on the protection scope of the present invention. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions may occur depending on design requirements and other factors. Any modification, equivalent substitution and improvement made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

[0134] It should be noted that the acquisition, storage and application of user personal information involved in the technical solution of the present disclosure are in compliance with the provisions of relevant laws and regulations and do not violate public order and good morals.

Claims

1. An item recommendation method, characterized in that: include: In response to the user corresponding to the item browsing request meeting the preset medical insurance condition, obtaining the user address information and item information corresponding to the item browsing request; Based on the user address information and item information, medical insurance item recommendation information associated with the item information is generated and displayed.

2. The method according to claim 1, characterized in that The user corresponding to the item browsing request meets the preset medical insurance conditions, including: Determine whether the user has a historical medical insurance record in the current region based on the acquired user history order information; or, query the medical insurance interface service based on the acquired user identity information, and determine whether the user has medical insurance qualifications based on the feedback result of the medical insurance interface service.

3. The method according to claim 1, characterized in that Based on the user address information and item information, generate and display medical insurance item recommendation information associated with the item information, including: Determine a target merchant set and a target item set based on the user address information and item information; wherein the target items are the same medical insurance items, similar medical insurance items, or matching medical insurance items; The recommendation coefficient of each target item is determined according to the target merchant set and the target item set, and the medical insurance item recommendation information is generated and displayed based on the recommendation coefficient of each target item.

4. The method according to claim 3, characterized in that: Determining a target merchant set and a target item set based on the user address information and the item information includes: Parsing the user address information to obtain longitude and latitude coordinates, and determining a first candidate merchant set based on the longitude and latitude coordinates; Determine a candidate item set corresponding to the item based on the item information and a preset logical scenario, and query a second candidate merchant set corresponding to the candidate item set; The intersection of the first candidate merchant set and the second candidate merchant set is taken, and a target merchant set and a target item set are determined based on the intersection result.

5. The method according to claim 4, characterized in that Determining a candidate item set corresponding to the item based on the item information and a preset logical scenario includes: Searching for identical medical insurance items that completely match the item based on the item information; In response to finding the same medical insurance item, generating a candidate item set based on the same medical insurance item; In response to not finding the same medical insurance item, similar medical insurance items that partially match the item are searched based on the item information and a preset similarity rule, and a candidate item set is generated based on the similar medical insurance items.

6. The method according to claim 5, characterized in that The method further comprises: In response to no similar medical insurance items being found, medical insurance items having a matching relationship with the items are searched based on the item information and a preset item matching relationship to generate a candidate item set.

7. The method according to claim 3, characterized in that Determining a recommendation coefficient for each target item according to the target merchant set and the target item set includes: Acquire the timeliness information of each target merchant in the target merchant set and the price information of each target item in the target item set; Normalizing the timeliness information of each target merchant and the price information of each target item; The normalized timeliness information of each target merchant and the price information of each target item are weighted and summed, and a recommendation coefficient of each target item is determined based on the calculation result.

8. The method according to claim 3, characterized in that Generate and display the medical insurance item recommendation information based on the recommendation coefficient of each target item, including: The recommendation coefficient of each target item is sorted, and the medical insurance item recommendation information is generated and displayed based on the sorting result.

9. An item recommendation device, characterized in that: include: An acquisition module, in response to the user corresponding to the item browsing request meeting the preset medical insurance condition, acquires the user address information and item information corresponding to the item browsing request; The recommendation module generates and displays the medical insurance item recommendation information associated with the item information based on the user address information and the item information.

10. An electronic device, characterized in that: include: one or more processors; a storage device for storing one or more programs, When the one or more programs are executed by the one or more processors, the one or more processors implement the method according to any one of claims 1 to 8.

11. A computer readable medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the method according to any one of claims 1 to 8 is implemented.

12. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the method according to any one of claims 1 to 8 is implemented.

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