Method and apparatus for transmitting recommendation information, storage medium, and electronic device

Abstract

The application discloses a sending method and device of recommendation information, a storage medium and an electronic device, relates to smart home, and the sending method of the recommendation information comprises the following steps: acquiring first operation data of a first object in a first client and second operation data of a plurality of second objects in a second client, the first operation data is used for indicating a first target position first position set operated by the first object in the first client and evaluation information of the first object to a first position in the first target position first position set; determining the similarity of the first object and the second object according to the first operation data and the second operation data, and determining a preset number of target second objects in the plurality of second objects according to the similarity; and sending the recommendation information of the target position to the first object according to the third operation data of the preset number of target second objects.

Description

Technical Field

[0001] This application relates to the field of communications, and more specifically, to a method and apparatus for transmitting recommendation information, a storage medium, and an electronic device. Background Technology

[0002] In recent years, the continuous upgrading and iteration of IoT technology has enabled the smart home industry to develop rapidly. Smart home products and services are gradually bringing users richer, higher-quality, and more effective recommendation services. In order to provide more accurate message recommendation services, smart home manufacturers and service providers are constantly exploring and researching message recommendation applications to meet the precise needs of various users.

[0003] Existing tourist attraction recommendation technologies typically recommend attractions to users based on their tag information. However, if the user tag information is incomplete, the accuracy of the recommended attractions will be greatly reduced.

[0004] There is currently no effective solution to the problem of low accuracy in recommending attractions to users based on their tag information in related technologies. Summary of the Invention

[0005] This application provides a method and apparatus for sending recommendation information, a storage medium, and an electronic device to at least solve the problem in the related art where the accuracy of recommended attractions is not high when recommending attractions to users based on user tag information.

[0006] According to one embodiment of this application, a method for sending recommendation information is provided, comprising: acquiring first operation data of a first object in a first client, and second operation data of a plurality of second objects in a second client, wherein the first client and the second client are the same or different clients, the first operation data being used to indicate a first set of locations operated by the first object in the first client, and evaluation information of the first object on a first location in the first set of locations, and the second operation data being used to indicate a second set of locations operated by the second object in the second client, and evaluation information of the second object on a second location in the second set of locations; determining the similarity between the first object and the second object based on the first operation data and the second operation data, and determining a preset number of target second objects among the plurality of second objects based on the similarity; sending recommendation information of target locations to the first object based on third operation data of the preset number of target second objects, wherein the third operation data being used to indicate a third set of locations operated by the target second object in the second client, and evaluation information of the target second object on a third location in the third set of locations.

[0007] In an exemplary embodiment, determining the similarity between the first object and the second object based on the first operation data and the second operation data includes: determining one or more fourth locations with the same name in the first location set and the second location set based on the first operation data and the second operation data; determining a first proportion of the one or more fourth locations in the first location set and a second proportion of the one or more fourth locations in the second location set; and determining the similarity between the first object and the second object based on the first proportion or the second proportion.

[0008] In an exemplary embodiment, determining the similarity between the first object and the second object based on the first operation data and the second operation data includes: determining one or more fifth locations in the first location set and the second location set based on the first operation data and the second operation data, wherein the fifth location is a target location that both the first object and the second object have operated on; determining first evaluation information of the first object on the one or more fifth locations and second evaluation information of the second object on the one or more fifth locations; determining a first similarity between the first evaluation information and the second evaluation information using a preset similarity model; determining the similarity between the first object and the second object based on the first similarity and the first ratio; or determining the similarity between the first object and the second object based on the first similarity and the second ratio.

[0009] In an exemplary embodiment, determining the similarity between the first object and the second object based on the first similarity and the first ratio; or determining the similarity between the first object and the second object based on the first similarity and the second ratio includes: calculating the sum of the first ratio and the reciprocal of the first similarity to obtain a first sum value, and using the first sum value as the similarity between the first object and the second object; or calculating the sum of the second ratio and the reciprocal of the first similarity to obtain a second sum value, and using the second sum value as the similarity between the first object and the second object.

[0010] In an exemplary embodiment, sending target location recommendation information to the first object based on the third operation data of the preset number of target second objects includes: determining a set of sixth locations that the first object has not operated on in the second location set based on the first operation data and the third operation data; sorting the multiple sixth locations in the sixth location set according to the rating information corresponding to the multiple sixth locations in the sixth location set; and sending the sorted set of sixth locations to the first object.

[0011] In one exemplary embodiment, sorting the plurality of sixth positions in the sixth position set according to the rating information corresponding to the plurality of sixth positions in the sixth position set includes: obtaining user information of the first object and predicting the interest type of the first object for the target position based on the user information; determining a set of seventh positions in the sixth position set that matches the interest type, and sorting the plurality of seventh positions according to the rating information corresponding to the plurality of seventh positions in the seventh position set.

[0012] In one exemplary embodiment, obtaining first operation data of a first object in a first client includes: detecting whether the first object has entered an operation on the first client to evaluate the first location, and / or whether it has browsed the first location on the first client; and if the operation of the first object to enter an operation on the first client to evaluate the first location is detected, and / or the operation of the first object to browse the first location is detected, obtaining the first operation data of the first object in the first client.

[0013] According to another embodiment of the present application, a device for sending recommendation information is also provided, comprising: an acquisition module, configured to acquire first operation data of a first object in a first client, and second operation data of a plurality of second objects in a second client respectively, wherein the first client and the second client are the same or different clients, the first operation data being used to indicate a first set of locations operated by the first object in the first client, and evaluation information of the first object on a first location in the first set of locations, and the second operation data being used to indicate a second set of locations operated by the second object in the second client, and evaluation information of the second object on a second location in the second set of locations; a determination module, configured to determine the similarity between the first object and the second object based on the first operation data and the second operation data, and determine a preset number of target second objects among the plurality of second objects based on the similarity; and a sending module, configured to send recommendation information of target locations to the first object based on third operation data of the preset number of target second objects, wherein the third operation data being used to indicate a third set of locations operated by the target second object in the second client, and evaluation information of the target second object on a third location in the third set of locations.

[0014] According to another aspect of the embodiments of this application, a computer-readable storage medium is also provided, wherein a computer program is stored in the computer program, and the computer program is configured to execute the above-described method for sending recommendation information when it is run.

[0015] According to another aspect of the embodiments of this application, an electronic device is also provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the above-described method for sending recommendation information through the computer program.

[0016] In this embodiment, first operation data of a first object in a first client and second operation data of multiple second objects in a second client are obtained. The first client and the second clients may be the same or different clients. The first operation data indicates a first set of locations operated on by the first object in the first client, and the first object's evaluation information for the first locations in the first set of locations. The second operation data indicates a second set of locations operated on by the second object in the second client, and the second object's evaluation information for the second locations in the second set of locations. The similarity between the first object and the second object is determined based on the first operation data and the second operation data, and the similarity is then used to... A preset number of target second objects are determined from a plurality of second objects; recommendation information of target locations is sent to the first object based on the third operation data of the preset number of target second objects, wherein the third operation data is used to indicate the set of third locations operated by the target second objects in the second client, and the evaluation information of the target second objects on the third locations of the set of third locations; by adopting the above technical solution, the problem of low accuracy of recommended attractions to users when recommending attractions to users based on user tag information is solved. Furthermore, this application uses a similarity algorithm to obtain a preset number of target second objects similar to the first object based on the operation data operated by the first object; and provides recommendation information to the first object based on the third operation data of the preset number of target second objects. Attached Figure Description

[0017] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0018] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, for those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0019] Figure 1 This is a schematic diagram of the hardware environment for a method of sending recommendation information according to an embodiment of this application;

[0020] Figure 2 This is a flowchart of a method for sending recommendation information according to an embodiment of this application;

[0021] Figure 3 This is a system structure block diagram of a method for sending recommendation information according to an embodiment of this application;

[0022] Figure 4This is a system flowchart of a method for sending recommendation information according to an embodiment of this application;

[0023] Figure 5 This is a system timing diagram of a method for sending recommendation information according to an embodiment of this application;

[0024] Figure 6 This is a structural block diagram of a recommendation information sending device according to an embodiment of this application. Detailed Implementation

[0025] To enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present application, and not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative effort should fall within the scope of protection of the present application.

[0026] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0027] According to one aspect of the embodiments of this application, a method for sending recommendation information is provided. This method for sending recommendation information is widely applicable to whole-house intelligent digital control application scenarios such as smart homes, smart home ecosystems, and intelligencehouse ecosystems. Optionally, in this embodiment, the above-mentioned method for sending recommendation information can be applied to, for example... Figure 1 The hardware environment shown consists of terminal device 102 and server 104. For example... Figure 1 As shown, server 104 is connected to terminal device 102 via a network and can be used to provide services (such as application services) to the terminal or clients installed on the terminal. A database can be set up on the server or independently of the server to provide data storage services for server 104. Cloud computing and / or edge computing services can be configured on the server or independently of the server to provide data processing services for server 104.

[0028] The aforementioned network may include, but is not limited to, at least one of the following: wired network, wireless network. The aforementioned wired network may include, but is not limited to, at least one of the following: wide area network, metropolitan area network, local area network. The aforementioned wireless network may include, but is not limited to, at least one of the following: Wi-Fi (Wireless Fidelity), Bluetooth. The terminal device 102 may not be limited to PC, mobile phone, tablet computer, smart air conditioner, smart range hood, smart refrigerator, smart oven, smart stove, smart washing machine, smart water heater, smart washing equipment, smart dishwasher, smart projector, smart TV, smart clothes rack, smart curtains, smart audio-visual equipment, smart socket, smart speaker, smart speaker box, smart fresh air equipment, smart kitchen and bathroom equipment, smart bathroom equipment, smart robot vacuum cleaner, smart window cleaning robot, smart mopping robot, smart air purifier, smart steam oven, smart microwave oven, smart water heater, smart air purifier, smart water dispenser, smart door lock, etc.

[0029] This embodiment provides a method for sending recommendation information, applied to a computer terminal. Figure 2 This is a flowchart of a method for sending recommendation information according to an embodiment of this application, which includes the following steps:

[0030] Step S202: Obtain first operation data of the first object in the first client, and second operation data of multiple second objects in the second client respectively, wherein the first client and the second client are the same or different clients. The first operation data is used to indicate the first set of locations operated by the first object in the first client, and the evaluation information of the first object on the first location in the first location set. The second operation data is used to indicate the second set of locations operated by the second object in the second client, and the evaluation information of the second object on the second location in the second location set.

[0031] It should be noted that the first operation data is used to indicate the first set of locations that the first object has commented on and / or browsed in the first client, as well as the evaluation information of the first object on the first location in the first set of locations. The second operation data is used to indicate the second set of locations that the second object has commented on and / or browsed in the second client, as well as the evaluation information of the second object on the second location in the second set of locations.

[0032] Step S204: Determine the similarity between the first object and the second object based on the first operation data and the second operation data, and determine a preset number of target second objects among the plurality of second objects based on the similarity.

[0033] Optionally, determining a preset number of target second objects from the plurality of second objects based on the similarity includes at least one of the following: sorting the similarities, determining the N highest similarities from the sorted similarities; determining the objects corresponding to the N highest similarities, and using the objects as the preset number of target second objects;

[0034] The similarity scores are determined to be greater than a preset threshold, and the objects corresponding to the similarity scores are determined to be greater than the preset threshold. These objects are then used as the preset number of target second objects.

[0035] Step S206: Send recommendation information of target location to the first object according to the third operation data of the preset number of target second objects, wherein the third operation data is used to indicate the set of third locations operated by the target second object in the second client, and the evaluation information of the target second object on the third location set.

[0036] Through the above steps, first operation data of a first object in a first client and second operation data of multiple second objects in a second client are obtained, wherein the first client and the second client are the same or different clients. The first operation data is used to indicate a first set of locations operated by the first object in the first client and the first object's evaluation information of the first location in the first set of locations. The second operation data is used to indicate a second set of locations operated by the second object in the second client and the second object's evaluation information of the second location in the second set of locations. The similarity between the first object and the second object is determined based on the first operation data and the second operation data, and the similarity is used to... A preset number of target second objects are determined from a plurality of second objects; recommendation information of target locations is sent to the first object based on the third operation data of the preset number of target second objects, wherein the third operation data is used to indicate the set of third locations operated by the target second objects in the second client, and the evaluation information of the target second objects on the third locations of the set of third locations. This solves the problem in related technologies that the accuracy of recommended attractions to users is not high when recommending attractions to users based on user tag information. Therefore, this application uses a similarity algorithm to obtain a preset number of target second objects similar to the first object based on the operation data operated by the first object; and provides recommendation information to the first object based on the third operation data of the preset number of target second objects.

[0037] As an optional approach, determining the similarity between the first object and the second object based on the first operation data and the second operation data includes:

[0038] S1, determine one or more fourth positions with the same name in the first position set and the second position set according to the first operation data and the second operation data;

[0039] S2, determine a first proportion of the one or more fourth positions in the first set of positions, and a second proportion of the one or more fourth positions in the second set of positions;

[0040] S3, determine the similarity between the first object and the second object based on the first ratio or the second ratio.

[0041] It should be noted that determining one or more fourth locations with the same name in the first location set and the second location set based on the first operation data and the second operation data can be understood as determining locations that have been viewed and / or browsed in both the first location set and the second location set.

[0042] Optionally, determining a first proportion of the one or more fourth positions in the first set of positions and a second proportion of the one or more fourth positions in the second set of positions includes: determining a first quantity of the first set of positions, a second quantity of the second set of positions, and a third quantity of the one or more fourth positions; determining a first proportion of the one or more fourth positions in the first set of positions based on a first quotient of the first quantity and the third quantity; and determining a second proportion of the one or more fourth positions in the second set of positions based on a second quotient of the second quantity and the third quantity.

[0043] For example, the browsing history information of the first object includes: the Forbidden City, the Great Wall, the Bird's Nest, Shichahai, Yuyuantan, etc.; the comment history information of the first object includes: the Forbidden City, Mutianyu Great Wall, the Bird's Nest, etc. (It should be noted that the Forbidden City, the Great Wall, the Bird's Nest, Shichahai, and Yuyuantan are equivalent to the first set of locations in the above embodiments); the browsing history information of the second object includes: the Great Wall, the Bird's Nest, Shichahai, Yuyuantan, the Art Museum, Mount Tai, Mount Huangshan, Mount Hengshan, etc.; the comment history information of the second object includes: Mount Tai, Mount Huangshan, Mount Hengshan, etc. (the Great Wall, the Bird's Nest, Shichahai, Yuyuantan, the Art Museum, Mount Tai, Mount Huangshan, and Mount Hengshan are equivalent to the second set of locations in the above embodiments); based on the browsing history information and comment history information of the first object and the second object, it is determined that both the first object and the second object have browsed and / or commented on one or more fourth locations: the Great Wall, the Bird's Nest, Shichahai, and Yuyuantan.

[0044] As an optional approach, determining the similarity between the first object and the second object based on the first operation data and the second operation data includes:

[0045] S1, determine one or more fifth positions in the first position set and the second position set based on the first operation data and the second operation data, wherein the one or more fifth positions are positions that have been operated on by both the first object and the second object;

[0046] It should be noted that one or more fifth positions can be positions that have been commented on by both the first and second objects.

[0047] S2, determine the first evaluation information of the first object on the one or more fifth positions and the second evaluation information of the second object on the one or more fifth positions;

[0048] S3, determine the first similarity between the first evaluation information and the second evaluation information using a preset similarity model;

[0049] S4, determine the similarity between the first object and the second object based on the first similarity and the first ratio; or determine the similarity between the first object and the second object based on the first similarity and the second ratio.

[0050] Optionally, before determining the first similarity between the first evaluation information and the second evaluation information through a preset similarity model, the method further includes: standardizing the first evaluation information and the second evaluation information so that the first evaluation information and the second evaluation information are integer values ​​within a target range, such as integer values ​​between [0, 10].

[0051] It should be noted that the above integer values ​​are used to indicate the degree of preference of the first object and the second object for one or more fifth positions.

[0052] Optionally, determining the first similarity between the first evaluation information and the second evaluation information using a preset similarity model includes: the preset similarity model determining the first similarity between the first evaluation information and the second evaluation information based on a first integer value corresponding to the first evaluation information and a second integer value corresponding to the second evaluation information.

[0053] Optionally, the first similarity s between the first evaluation information and the second evaluation information is determined according to the following calculation formula. 12 :

[0054] Wherein, n indicates the number of the one or more fifth positions, n = 1, ...; x 1k x is the first integer value corresponding to the first evaluation information; 2k d is the second integer value corresponding to the second evaluation information. 12The Euclidean distance between the first evaluation information and the second evaluation information is given.

[0055] As an optional approach, the similarity between the first object and the second object is determined based on the first similarity and the first ratio; or the similarity between the first object and the second object is determined based on the first similarity and the second ratio, including: calculating the sum of the first ratio and the reciprocal of the first similarity to obtain a first sum value, and using the first sum value as the similarity between the first object and the second object; or calculating the sum of the second ratio and the reciprocal of the first similarity to obtain a second sum value, and using the second sum value as the similarity between the first object and the second object.

[0056] It should be noted that the similarity between the second object and the first object is r² + 1 / s. 12 Wherein, r2 is the second ratio;

[0057] The similarity between the first object and the second object = r1 + 1 / s 12 Wherein, r2 is the first ratio.

[0058] As an optional approach, sending target location recommendation information to the first object based on the third operation data of the preset number of target second objects includes: determining a set of sixth locations that the first object has not operated on in the second location set based on the first operation data and the third operation data; sorting the multiple sixth locations in the sixth location set according to the rating information corresponding to the multiple sixth locations in the sixth location set; and sending the sorted set of sixth locations to the first object.

[0059] For example, if the target second object is among the top 100 objects in terms of similarity, obtain the browsing and commenting data of these 100 users. Remove the target positions that have already been commented on by the first object, and rank them in reverse order according to the rating information of the sixth position to obtain the sorted set of the sixth positions, as well as the set of the top 10 sixth positions with the highest ratings.

[0060] As an optional approach, sorting the multiple sixth positions in the set of sixth positions according to the rating information corresponding to each of the multiple sixth positions in the set of sixth positions includes: obtaining user information of the first object and predicting the interest type of the first object for the target position based on the user information; determining a set of seventh positions in the set of sixth positions that matches the interest type, and sorting the multiple seventh positions according to the rating information corresponding to each of the multiple seventh positions in the set of seventh positions.

[0061] This invention provides a method for determining a set of seventh positions to recommend to a first object by combining the interest type of a first object and a set of sixth positions. Specifically, the interest type of the first object is determined based on user information, and a set of seventh positions matching the interest type is determined from the set of sixth positions.

[0062] As an optional approach, obtaining the first operation data of the first object in the first client includes: detecting whether the first object has entered an operation to evaluate the first location in the first client, and / or whether it has browsed the first location in the first client; if it is detected that the first object has entered an operation to evaluate the first location in the first client, and / or that the first object has browsed the first location in the first client, obtaining the first operation data of the first object in the first client.

[0063] To better understand the process of sending the above-mentioned recommendation information, the implementation flow of sending the above-mentioned recommendation information will be described below in conjunction with optional embodiments, but this is not intended to limit the technical solution of the embodiments of this application.

[0064] This embodiment provides a method for sending recommendation information. Figure 3 This is a system structure block diagram of a method for sending recommendation information according to an embodiment of this application, such as... Figure 3 As shown, this system includes at least: an information receiving device, a cloud server, a Hadoop server, a message push server, and a recommendation message receiving device.

[0065] This embodiment provides a method for sending recommendation information. Figure 4 This is a system flowchart of a method for sending recommendation information according to an embodiment of this application, such as... Figure 4 As shown, the specific steps are as follows:

[0066] Step S401: Begin;

[0067] Step S402: The first object registers in the information collection device and the recommendation message receiving device;

[0068] In other words, it is necessary to bind the information collection device and the recommendation message receiving device to the first object in advance so that there is an effective mapping relationship between the collection device and the receiving device;

[0069] Step S403: Obtain the browsing history and comment history of the first object (equivalent to the first operation data in the above embodiment);

[0070] Specifically, the data is uploaded through a cloud server and then loaded onto a Hadoop server for data storage and processing.

[0071] Step S404: Based on the browsing and commenting history of the first object, perform user similarity analysis to obtain the top 100 user groups with the highest similarity, and obtain a list of tourist attractions browsed and commented on by the top 100 user groups (equivalent to the target location indicated by the third operation data in the above embodiment); compare the list of tourist attractions with the tourist attraction information that the user has commented on to obtain a list of recommended tourist attractions that the user has not commented on, so as to provide the user with a message recommendation service that conforms to the user's habits;

[0072] Step S405: Push the list of recommended tourist attractions to the user's message receiving device via the message push server;

[0073] Step S406: End.

[0074] Figure 5 This is a system timing diagram of a method for sending recommendation information according to an embodiment of this application, such as... Figure 4 As shown, the specific steps are as follows:

[0075] Step S501: First object information processing;

[0076] 1.1 Standardizing the existing first object information can identify a unique first object.

[0077] Step S502: Process the browsing and comment history information of the first object;

[0078] 2.1 Processing of First Object Browsing Record Information: Based on the first object's browsing record information a(k), the tourist attraction information browsed by the first object is cleaned to obtain standard tourist attraction information. Examples include the Forbidden City, Mutianyu Great Wall, and the Bird's Nest.

[0079] 2.2 Processing of First Object Comment Record Information: Based on the first object comment record information b(k), the first object comment score information is standardized and unified to an integer value between 1 and 5. For example, Forbidden City: 5, Bird's Nest: 4.

[0080] Step S503: Similarity calculation;

[0081] 3.1 Calculation of user browsing history similarity: Compare the browsing history of two different users U1 and U2 to obtain the same tourist attraction information in the browsing history of the two users, and the proportion of the same tourist attraction information in the total browsing history of the two users, r1 and r2 respectively.

[0082] 3.2 User Review Record Similarity Calculation: The review records of two different users, U1 and U2, are compared. The review records of the two different users are obtained separately, including comments on tourist attractions and review scores. Using the two review scores for the same tourist attraction from both users as parameters, the similarity result 's' of the user review records is calculated according to the similarity model.

[0083] The similarity model uses the Euclidean distance formula to calculate similarity; the smaller the distance, the higher the similarity.

[0084] Euclidean distance formula: Wherein, n indicates the number of identical attractions, n = 1, ...; x 1k x is the first integer value corresponding to the evaluation information of the first object; 2k The second integer value corresponding to the evaluation information of the first object; s = 1 - d 12 ;

[0085] 3.3 Determine the top 100 users with the highest similarity:

[0086] Similarity between user U2 and U1 = r2 + 1 / s;

[0087] Similarity between user U1 and U2 = r1 + 1 / s;

[0088] Get the list of the top 100 users with the highest similarity to the current user U1.

[0089] 3.4 List of recommended tourist attractions.

[0090] Based on the information of the top 100 users with the highest similarity to the current user, obtain the tourist attraction information viewed and commented on by these 100 users. Remove the tourist attraction information already commented on by the current user, and rank the tourist attractions in descending order of their ratings to obtain the information of the top 10 tourist attractions with the highest ratings.

[0091] This application performs similarity analysis on users based on their browsing and commenting behavior information, and matches the tourist attraction information of the top 100 users with the highest similarity to the tourist attractions they have browsed and commented on. It then recommends tourist attractions that users have not commented on to them, thus creating personalized recommendations for each user.

[0092] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods according to the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) and includes several instructions to cause a terminal device (which may be a mobile phone, computer, server, or network device, etc.) to execute the methods of the various embodiments of this application.

[0093] Figure 6 This is a structural block diagram of a recommendation information sending device according to an embodiment of this application; as shown below. Figure 4 As shown, it includes:

[0094] The acquisition module 62 is used to acquire first operation data of a first object in a first client and second operation data of multiple second objects in a second client respectively, wherein the first client and the second client are the same or different clients, the first operation data is used to indicate a first set of locations operated by the first object in the first client and the evaluation information of the first object on the first location in the first location set, and the second operation data is used to indicate a second set of locations operated by the second object in the second client and the evaluation information of the second object on the second location in the second location set;

[0095] The determining module 64 is used to determine the similarity between the first object and the second object based on the first operation data and the second operation data, and to determine a preset number of target second objects among the plurality of second objects based on the similarity.

[0096] The sending module 66 is used to send recommendation information of target locations to the first object based on the third operation data of the preset number of target second objects, wherein the third operation data is used to indicate the set of third locations operated by the target second object in the second client, and the evaluation information of the target second object on the third locations of the set of third locations.

[0097] Using the aforementioned apparatus, first operation data of a first object in a first client and second operation data of multiple second objects in second clients are acquired, wherein the first client and the second clients may be the same or different clients. The first operation data is used to indicate a first set of locations operated on by the first object in the first client, and the first object's evaluation information of the first location in the first set of locations. The second operation data is used to indicate a second set of locations operated on by the second object in the second client, and the second object's evaluation information of the second location in the second set of locations. The similarity between the first object and the second object is determined based on the first operation data and the second operation data, and the similarity is then used to... A preset number of target second objects are determined from a plurality of second objects; recommendation information of target locations is sent to the first object based on the third operation data of the preset number of target second objects, wherein the third operation data is used to indicate the set of third locations operated by the target second objects in the second client, and the evaluation information of the target second objects on the third locations of the set of third locations. This solves the problem in related technologies that the accuracy of recommended attractions to users is not high when recommending attractions to users based on user tag information. Therefore, this application uses a similarity algorithm to obtain a preset number of target second objects similar to the first object based on the operation data operated by the first object; and provides recommendation information to the first object based on the third operation data of the preset number of target second objects.

[0098] In an exemplary embodiment, the determining module 64 is configured to determine one or more fourth locations with the same name in the first location set and the second location set based on the first operation data and the second operation data; determine a first proportion of the one or more fourth locations in the first location set and a second proportion of the one or more fourth locations in the second location set; and determine the similarity between the first object and the second object based on the first proportion or the second proportion.

[0099] In an exemplary embodiment, the determining module 64 is configured to determine one or more fifth locations in the first location set and the second location set based on the first operation data and the second operation data, wherein the fifth location is a location that both the first object and the second object have operated on; determine first evaluation information of the first object on the one or more fifth locations and second evaluation information of the second object on the one or more fifth locations; determine a first similarity between the first evaluation information and the second evaluation information using a preset similarity model; determine the similarity between the first object and the second object based on the first similarity and the first ratio; or determine the similarity between the first object and the second object based on the first similarity and the second ratio.

[0100] In an exemplary embodiment, the determining module 64 is configured to calculate the sum of the first ratio and the reciprocal of the first similarity to obtain a first sum value, and use the first sum value as the similarity between the first object and the second object; or to calculate the sum of the second ratio and the reciprocal of the first similarity to obtain a second sum value, and use the second sum value as the similarity between the first object and the second object.

[0101] In an exemplary embodiment, the sending module 66 is configured to determine, based on the first operation data and the third operation data, a set of sixth positions that the first object has not operated on in the second position set; sort the plurality of sixth positions in the set according to the scoring information corresponding to the plurality of sixth positions in the set; and send the sorted set of sixth positions to the first object.

[0102] In an exemplary embodiment, the determining module 64 is configured to acquire user information of the first object and predict the type of interest of the first object to the target location based on the user information; determine a set of seventh locations that match the type of interest in the sixth location set; and rank the plurality of seventh locations according to the rating information corresponding to the plurality of seventh locations in the seventh location set.

[0103] In an exemplary embodiment, the acquisition module 62 is configured to detect whether the first object has input an operation on the first client to evaluate the first location, and / or whether it has browsed the first location on the first client; and if it is detected that the first object has input an operation on the first client to evaluate the first location set, and / or that the first object has browsed the first location on the first client, it acquires first operation data of the first object in the first client.

[0104] Embodiments of this application also provide a storage medium including a stored program, wherein the program executes any of the methods described above when it is run.

[0105] Optionally, in this embodiment, the storage medium may be configured to store program code for performing the following steps:

[0106] S1, obtain the first operation data of the first object in the first client, and the second operation data of the multiple second objects in the second client respectively;

[0107] S2, determine the similarity between the first object and the second object based on the first operation data and the second operation data, and determine a preset number of target second objects among the plurality of second objects based on the similarity;

[0108] S3, based on the third operation data of the preset number of target second objects, send the recommended information of the target location to the first object.

[0109] Embodiments of this application also provide an electronic device including a memory and a processor, wherein the memory stores a computer program and the processor is configured to run the computer program to perform the steps in any of the above method embodiments.

[0110] Optionally, the electronic device may further include a transmission device and an input / output device, wherein the transmission device is connected to the processor and the input / output device is connected to the processor.

[0111] Optionally, in this embodiment, the processor can be configured to perform the following steps via a computer program:

[0112] S1, obtain the first operation data of the first object in the first client, and the second operation data of the multiple second objects in the second client respectively;

[0113] S2, determine the similarity between the first object and the second object based on the first operation data and the second operation data, and determine a preset number of target second objects among the plurality of second objects based on the similarity;

[0114] S3, based on the third operation data of the preset number of target second objects, send the recommended information of the target location to the first object.

[0115] Optionally, in this embodiment, the storage medium may include, but is not limited to, various media capable of storing program code, such as USB flash drives, read-only memory (ROM), random access memory (RAM), portable hard drives, magnetic disks, or optical disks.

[0116] Optionally, specific examples in this embodiment can refer to the examples described in the above embodiments and optional implementations, and will not be repeated here.

[0117] Obviously, those skilled in the art should understand that the modules or steps of this application described above can be implemented using general-purpose computing devices. They can be centralized on a single computing device or distributed across a network of multiple computing devices. Optionally, they can be implemented using computer-executable program code, thereby storing them in a storage device for execution by a computing device. In some cases, the steps shown or described can be performed in a different order than those presented here, or they can be fabricated as separate integrated circuit modules, or multiple modules or steps can be fabricated as a single integrated circuit module. Thus, this application is not limited to any particular combination of hardware and software.

[0118] The above description is only a preferred embodiment of this application. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of this application, and these improvements and modifications should also be considered within the scope of protection of this application.

Claims

1. A method for sending recommendation information, characterized in that, include: The system acquires first operation data of a first object in a first client and second operation data of multiple second objects in a second client, wherein the first client and the second client are the same or different clients. The first operation data is used to indicate a first set of locations operated by the first object in the first client and the evaluation information of the first object on the first location in the first location set. The second operation data is used to indicate a second set of locations operated by the second object in the second client and the evaluation information of the second object on the second location in the second location set. The similarity between the first object and the second object is determined based on the first operation data and the second operation data, and a preset number of target second objects are determined among the plurality of second objects based on the similarity. Recommendation information of target locations is sent to the first object based on the third operation data of the preset number of target second objects, wherein the third operation data is used to indicate the set of third locations operated by the target second object in the second client, and the evaluation information of the target second object on the third locations of the set of third locations; Determining the similarity between the first object and the second object based on the first operation data and the second operation data includes: determining one or more fourth positions with the same name in the first position set and the second position set based on the first operation data and the second operation data; determining a first proportion of the one or more fourth positions in the first position set and a second proportion of the one or more fourth positions in the second position set; and determining the similarity between the first object and the second object based on the first proportion or the second proportion. Determining the similarity between the first object and the second object based on the first operation data and the second operation data includes: determining one or more fifth positions in the first position set and the second position set based on the first operation data and the second operation data, wherein the fifth positions are positions that both the first object and the second object have operated on; determining first evaluation information of the first object on the one or more fifth positions and second evaluation information of the second object on the one or more fifth positions; determining a first similarity between the first evaluation information and the second evaluation information through a preset similarity model; determining the similarity between the first object and the second object based on the first similarity and the first ratio; or determining the similarity between the first object and the second object based on the first similarity and the second ratio. The method of determining the similarity between the first object and the second object based on the first similarity and the first ratio, or determining the similarity between the first object and the second object based on the first similarity and the second ratio, includes: calculating the sum of the first ratio and the reciprocal of the first similarity to obtain a first sum value, and using the first sum value as the similarity between the first object and the second object; or calculating the sum of the second ratio and the reciprocal of the first similarity to obtain a second sum value, and using the second sum value as the similarity between the first object and the second object.

2. The method for sending recommendation information according to claim 1, characterized in that, Based on the third operation data of the preset number of target second objects, recommendation information of the target location is sent to the first object, including: Based on the first operation data and the third operation data, determine the sixth location set in the second location set that the first object has not been operated on; Sort the multiple sixth positions in the set of sixth positions according to the scoring information corresponding to each of the multiple sixth positions in the set of sixth positions; Send the sorted set of the sixth position to the first object.

3. The method for sending recommendation information according to claim 2, characterized in that, Sort the multiple sixth positions in the set of sixth positions according to the scoring information corresponding to each of the multiple sixth positions, including: Obtain user information of the first object, and predict the type of interest of the first object in the target location based on the user information; In the sixth set of positions, a set of seventh positions that matches the type of interest is determined, and the plurality of seventh positions are sorted according to the rating information corresponding to the plurality of seventh positions in the set of seventh positions.

4. The method for sending recommendation information according to claim 1, characterized in that, Retrieve the first operation data of the first object in the first client, including: Detect whether the first object has entered evaluation information about the first location on the first client, and / or whether it has browsed the first location on the first client; If the operation of the first object to input evaluation information about the first location is detected in the first client, and / or if the operation of the first object to browse the first location is detected in the first client, the first operation data of the first object in the first client is obtained.

5. A device for sending recommendation information, characterized in that, include: The acquisition module is used to acquire first operation data of a first object in a first client, and second operation data of multiple second objects in a second client respectively, wherein the first client and the second client are the same or different clients. The first operation data is used to indicate a first set of locations operated by the first object in the first client, and the first object's evaluation information on the first location in the first set of locations. The second operation data is used to indicate a second set of locations operated by the second object in the second client, and the second object's evaluation information on the second location in the second set of locations. The determining module is configured to determine the similarity between the first object and the second object based on the first operation data and the second operation data, and to determine a preset number of target second objects among the plurality of second objects based on the similarity. The sending module is used to send recommendation information of target locations to the first object based on the third operation data of the preset number of target second objects, wherein the third operation data is used to indicate the set of third locations operated by the target second object in the second client, and the evaluation information of the target second object on the third locations of the set of third locations; The determining module is further configured to: determine one or more fourth positions with the same name in the first position set and the second position set based on the first operation data and the second operation data; determine a first proportion of the one or more fourth positions in the first position set and a second proportion of the one or more fourth positions in the second position set; and determine the similarity between the first object and the second object based on the first proportion or the second proportion. The determining module is further configured to: determine one or more fifth positions in the first position set and the second position set based on the first operation data and the second operation data, wherein the fifth position is a position that both the first object and the second object have operated on; determine first evaluation information of the first object on the one or more fifth positions and second evaluation information of the second object on the one or more fifth positions; determine a first similarity between the first evaluation information and the second evaluation information through a preset similarity model; determine the similarity between the first object and the second object based on the first similarity and the first ratio; or determine the similarity between the first object and the second object based on the first similarity and the second ratio. The determining module is further configured to calculate the sum of the first ratio and the reciprocal of the first similarity to obtain a first sum value, and use the first sum value as the similarity between the first object and the second object; or to calculate the sum of the second ratio and the reciprocal of the first similarity to obtain a second sum value, and use the second sum value as the similarity between the first object and the second object.

6. A computer-readable storage medium, characterized in that, The computer-readable storage medium includes a stored program, wherein the program, when executed, performs the method described in any one of claims 1 to 4.

7. An electronic device comprising a memory and a processor, characterized in that, The memory stores a computer program, and the processor is configured to execute the method described in any one of claims 1 to 4 through the computer program.

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