CN113360790A - Information recommendation method and device and electronic equipment - Google Patents
Information recommendation method and device and electronic equipment Download PDFInfo
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- CN113360790A CN113360790A CN202110639366.3A CN202110639366A CN113360790A CN 113360790 A CN113360790 A CN 113360790A CN 202110639366 A CN202110639366 A CN 202110639366A CN 113360790 A CN113360790 A CN 113360790A
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/90—Details of database functions independent of the retrieved data types
- G06F16/95—Retrieval from the web
- G06F16/953—Querying, e.g. by the use of web search engines
- G06F16/9537—Spatial or temporal dependent retrieval, e.g. spatiotemporal queries
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/90—Details of database functions independent of the retrieved data types
- G06F16/95—Retrieval from the web
- G06F16/953—Querying, e.g. by the use of web search engines
- G06F16/9535—Search customisation based on user profiles and personalisation
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q30/00—Commerce
- G06Q30/06—Buying, selling or leasing transactions
- G06Q30/0601—Electronic shopping [e-shopping]
Abstract
The application discloses an information recommendation method and device and electronic equipment, and relates to the technical field of information processing. The method comprises the following steps: a server receives a shop recommendation request, wherein the shop recommendation request carries position information; acquiring platform data of each target shop within a preset range of the position information, wherein the platform data comprises: store data, historical order data and corresponding order user data of the target store; determining the respective corresponding shop grades of the target shops based on a plurality of rating dimensions according to the platform data; and returning the recommendation information of the target shop of which the shop grade meets the preset grade condition to the client. The method and the system can achieve accuracy and comprehensiveness of store information recommendation.
Description
Technical Field
The present application relates to the field of information processing technologies, and in particular, to an information recommendation method and apparatus, and an electronic device.
Background
With the development of internet technology, the internet is capable of providing more and more network services to users, for example, users can browse videos, listen to music, read, shop, and the like through the internet. In the internet platform, a user can search for information required by the user through a search function. Meanwhile, in order to facilitate the user to acquire information, the internet platform can actively recommend information to the user.
At present, the interest degree of a user can be analyzed by acquiring the information browsing record of the user, and then store information recommendation is carried out on the user according to the interest degree.
However, if the data recorded in the information browsing of the user is less or none or wrong, the analyzed user interest level may be inaccurate or the user interest level may not be obtained, and thus the accuracy of the store information recommendation may be affected, which may result in that the recommended information is wrong information, not only waste resources called by the information recommendation, but also the user may not obtain the store recommendation information needed by the user in the webpage at the first time, thereby increasing the cost of finding the needed information.
Disclosure of Invention
In view of this, the present application provides an information recommendation method, an information recommendation device and an electronic device, and mainly aims to solve the technical problems that the accuracy of store information recommendation is affected, resources called by information recommendation are wasted, and the cost of searching for required information by a user is increased in the prior art.
According to an aspect of the present application, there is provided an information recommendation method, applicable to a server side, the method including:
receiving a shop recommendation request, wherein the shop recommendation request carries position information;
acquiring platform data of each target shop within a preset range of the position information, wherein the platform data comprises: store data, historical order data and corresponding order user data of the target store;
determining the respective corresponding shop grades of the target shops based on a plurality of rating dimensions according to the platform data;
and returning the recommendation information of the target shop of which the shop grade meets the preset grade condition.
Optionally, the determining, according to the platform data, the respective corresponding store grades of the target stores based on the multiple rating dimensions specifically includes:
obtaining the dimension number of the target shop reaching the rating standard in a plurality of rating dimensions according to the platform data;
and determining the shop grade corresponding to the target shop according to the dimension number of the qualified rating.
Optionally, the obtaining, according to the platform data, the number of dimensions of the target store reaching the rating standard in the plurality of rating dimensions specifically includes:
calculating scores corresponding to a plurality of rating dimensions of target shops according to the platform data;
for each rating dimension, judging whether the score corresponding to the target store is larger than or equal to the threshold value corresponding to the rating dimension, if so, determining that the rating of the target store laid on the rating dimension reaches the standard;
and counting the dimension number of the target store rating reaching the standard.
Optionally, before determining, for each rating dimension, whether the score corresponding to the target store is greater than or equal to the threshold corresponding to the rating dimension, the method further includes:
and integrally determining the threshold values corresponding to the plurality of rating dimensions according to the recommended number of stores, the grade gear distribution and the priorities among the plurality of rating dimensions within the preset range of the position information.
Optionally, the integrally determining, according to the store recommendation number, the level gear distribution, and the priorities among the plurality of rating dimensions within the preset range of the location information, thresholds corresponding to the plurality of rating dimensions, specifically includes:
according to the sequence of the priorities among the plurality of rating dimensions from high to low, Z-shaped searching is carried out according to the shop scores of all shops within the preset range of the position information, and the searching is stopped until the number of the searched shops meets the stop condition, wherein the stop condition is determined according to the recommended number of the shops and the grade gear distribution;
and determining a threshold value corresponding to each of the plurality of rating dimensions by referring to the shop score finally searched after the plurality of rating dimensions are stopped.
Optionally, the calculating, according to the platform data, scores corresponding to a plurality of rating dimensions of the target store, specifically includes:
if the rating dimension is the first dimension of the local specific user behavior, determining the rating corresponding to the first dimension of the target store according to the number of consumers of the local specific user of the target store in a preset time period;
if the rating dimension is the second dimension of the behavior of the foreign users, determining the corresponding rating of the target store in the second dimension according to the product of the average consumption of the foreign users of the target store and the amount of orders for the foreign users with the maximum interval of days recorded by orders less than or equal to a preset threshold of days;
if the rating dimension is a third dimension of local user behaviors, determining a score corresponding to the third dimension of the target store according to the ratio of the number of local users who consume the stores outside the specific range and have the times larger than a preset time threshold value to the total number of local users who consume the target store;
if the rating dimension is the fourth dimension of all user behaviors, determining the score of the target store corresponding to the fourth dimension according to the product of all orders of the target store and the per-person consumption;
and if the rating dimension is the fifth dimension of the store comment, determining the grade of the target store corresponding to the fifth dimension according to the user comment grade of the target store.
Optionally, the step of returning the recommendation information of the target store of which the store level meets the preset level condition specifically includes:
obtaining target shops with the shop levels larger than a preset level threshold value, and screening according to the number of shops needing to be recommended in each level gear;
and returning the recommendation information of the screened target shop.
According to another aspect of the present application, there is provided an information recommendation method, applicable to a client side, the method including:
sending a shop recommendation request, wherein the shop recommendation request carries position information, so that according to platform data of each target shop within a preset range of the position information, the respective corresponding shop level of the target shop is determined based on a plurality of rating dimensions, and the platform data comprises: store data, historical order data and corresponding order user data of the target store;
and receiving recommendation information of the target shop of which the shop grade meets the preset grade condition.
Optionally, after receiving the recommendation information of the target store whose store level meets the preset level condition, the method further includes:
and outputting the received recommendation information of the target stores and the corresponding store grade marks of the target stores.
According to still another aspect of the present application, there is provided an information recommendation apparatus, applicable to a service side, the apparatus including:
the system comprises a receiving module, a processing module and a display module, wherein the receiving module is used for receiving a shop recommendation request which carries position information;
the acquisition module is used for acquiring platform data of each target shop within a preset range of the position information, wherein the platform data comprise: store data, historical order data and corresponding order user data of the target store;
the determining module is used for determining the corresponding shop grade of the target shop based on a plurality of rating dimensions according to the platform data;
and the sending module is used for returning the recommendation information of the target shop of which the shop grade meets the preset grade condition.
According to still another aspect of the present application, there is provided an information recommendation apparatus applicable to a client side, the apparatus including:
the system comprises a sending module and a receiving module, wherein the sending module is used for sending a shop recommendation request, and the shop recommendation request carries position information, so that the respective corresponding shop levels of target shops are determined based on a plurality of rating dimensions according to platform data of each target shop within a preset range of the position information, and the platform data comprises: store data, historical order data and corresponding order user data of the target store;
the receiving module is used for receiving the recommendation information of the target shop of which the shop grade meets the preset grade condition.
According to yet another aspect of the present application, there is provided a storage medium having stored thereon a computer program which, when executed by a processor, implements the information recommendation method described above.
In accordance with yet another aspect of the present application, there is provided an electronic device, which may include: the server device or the client device specifically includes a storage medium, a processor, and a computer program stored on the storage medium and capable of running on the processor, and the processor implements the information recommendation method when executing the computer program.
By means of the technical scheme, compared with the conventional mode, the information recommendation method, the information recommendation device and the electronic equipment do not need to rely on information browsing records of users to perform store information recommendation, and reference data adopted by the information recommendation method are platform data of each target store in a position range and specifically comprise store data of the target store, historical order data and corresponding order user data. And determining the corresponding shop grade of the target shop based on the plurality of rating dimensions according to the reference data, and recommending the target shop information of which the shop grade meets the preset grade condition to the user in consideration of different rating dimensions. On one hand, the accuracy and comprehensiveness of shop information recommendation can be achieved, and the cost of searching the required information by the user is saved; on the other hand, the method can reduce the invasion of the privacy data of the user and protect the privacy of the user.
The foregoing description is only an overview of the technical solutions of the present application, and the present application can be implemented according to the content of the description in order to make the technical means of the present application more clearly understood, and the following detailed description of the present application is given in order to make the above and other objects, features, and advantages of the present application more clearly understandable.
Drawings
The accompanying drawings, which are included to provide a further understanding of the application and are incorporated in and constitute a part of this application, illustrate embodiment(s) of the application and together with the description serve to explain the application and not to limit the application. In the drawings:
fig. 1 is a schematic flowchart illustrating an information recommendation method provided in an embodiment of the present application;
FIG. 2 illustrates a store rating algorithm model diagram provided by an embodiment of the present application;
FIG. 3 is a flowchart illustrating another information recommendation method provided in an embodiment of the present application;
FIG. 4 is a diagram illustrating an example effect of an information recommendation presentation provided by an embodiment of the present application;
fig. 5 is a schematic structural diagram illustrating an information recommendation apparatus according to an embodiment of the present application;
fig. 6 shows a schematic structural diagram of another information recommendation device provided in an embodiment of the present application.
Detailed Description
The present application will be described in detail below with reference to the accompanying drawings in conjunction with embodiments. It should be noted that the embodiments and features of the embodiments in the present application may be combined with each other without conflict.
The technical problems that the accuracy of shop information recommendation is affected, resources called by information recommendation are wasted, and the cost of searching required information by a user is increased in the prior art are solved. The embodiment provides an information recommendation method, as shown in fig. 1, applicable to a server side, the method including:
The store recommendation request carries position information, the position information can be position information of a designated searching store, and can be specifically set by default of a system, or manually selected by a user, or determined by a client according to current positioning information of a terminal where the client is located. The client can be configured on the intelligent terminal side of a user, such as a smart phone, a tablet computer, a smart watch, a smart bracelet and the like.
And 102, the server side obtains platform data of each target store within a preset range of the position information in the store recommendation request.
The preset range may be set by system default, or manually selected by a user, etc. Platform data of each target shop in the range of the city, the district/county, 2 kilometers nearby, 500 meters nearby and the like of the designated position is obtained by searching.
The platform data of the targeted store may specifically include: store data of the target store (such as store name, location, store type, specification standard and the like), historical order data (such as historical order information for the target store, which can include order content, order time and the like) and corresponding order user data (such as user identification of an order consuming user, a regular premises, other store orders of the user and the like, wherein the regular premises can be obtained by calculating each order time and the order store location of the user within a period of time).
And 103, the server determines the corresponding store grades of the target stores based on a plurality of rating dimensions according to the acquired platform data of the target stores.
The rating dimension can be determined according to actual needs, and the embodiment can comprehensively analyze the store grades of the target stores by taking the store data, the historical order data and the order user data of the target stores into consideration from a plurality of different rating dimensions as reference bases. Wherein different store grades may represent different store quality criteria.
And step 104, the server returns the recommendation information of the target shop of which the shop level meets the preset level condition to the client.
The preset grade condition can be preset according to actual requirements, such as default setting of a system or personalized setting of a client user (so that accurate shop recommendation meeting the personalized requirements of the user can be achieved), and the preset grade condition can be used for target shops with quality meeting the recommendation requirements of the user.
The shop recommendation information returned by the server side can comprise the name, the position, the shop characteristics, the shop grade standard and the like of the recommended shop. And the client outputs the returned shop recommendation information.
Compared with the conventional method, the information recommendation method provided by the embodiment does not need to rely on information browsing records of users to perform store information recommendation, and the reference data adopted by the embodiment is platform data of each target store in a certain position range, and specifically comprises store data of the target store, historical order data and corresponding order user data. And determining the corresponding shop grade of the target shop based on the plurality of rating dimensions according to the reference data, and recommending the target shop information of which the shop grade meets the preset grade condition to the user in consideration of different rating dimensions. On one hand, the accuracy and comprehensiveness of shop information recommendation can be achieved, and the cost of searching the required information by the user is saved; on the other hand, the method can reduce the invasion of the privacy data of the user and protect the privacy of the user.
Further, as a refinement and an extension of the specific implementation of the above embodiment, optionally, step 103 may specifically include: the method comprises the steps that a server side firstly obtains the number of dimensions of a target store reaching a rating standard in a plurality of rating dimensions according to obtained platform data of each target store; and then determining the corresponding shop grade of the target shop according to the dimension number of the qualified grade.
For example, each rating dimension has a respective rating criterion, wherein a level a may represent "highest level", a level B may represent "medium", and a level C may represent "poor".
An alternative is that if a store is rated for a rating dimension that meets the class a criteria, the store may be considered to be rated up in that rating dimension. If 6 rating dimensions are provided, and the shop 1 is rated as a level a in all the 6 rating dimensions, the shop 1 is considered as a 6A shop, and the shop level of the shop 1 is determined to be 6A; if the store 2 is rated as a level a in 4 of the 6 rating dimensions, the store 2 is considered to be a 4A store, and the store level of the store 2 is determined to be 4A.
Alternatively, if a store is rated for a rating dimension that meets criteria above (including level B), the store may be considered rated up in that rating dimension. And then comprehensively determining the corresponding shop grade of the target shop according to the dimension number of the qualified rating and the specific rating information of each rating dimension. If there are 6 rating dimensions, and store 3 has 3 levels a, 2 levels B, and 1 level C in the 6 rating dimensions, then the store 3 may be considered a medium-sized store; store 4 has 1 level a, 2 levels B, and 2 levels C in these 6 rating dimensions, then the store 4 may be considered a medium-sized store.
Further optionally, different weights are provided between different rating dimensions, and if the weight of the rating dimension is larger, the rating dimension is more important, and the influence on the rating of the shop is larger. Furthermore, when the shop level is determined, the shop level corresponding to the target shop can be determined comprehensively according to the number of dimensions of which the rating reaches the standard, the specific rating information of each rating dimension and the weight of each rating dimension, so that the more accurate shop level can be obtained.
In order to accurately determine whether the rating of the target store meets the standard for the rating dimension, as another optional manner, the server obtains, according to the obtained platform data of each target store, the number of dimensions of the target store meeting the standard in the rating dimensions, which may specifically include: firstly, calculating scores respectively corresponding to a plurality of rating dimensions laid by a target store according to the acquired platform data of the target store; then, judging whether the score corresponding to the target store is larger than or equal to the threshold value corresponding to the rating dimension or not aiming at each rating dimension, and if so, determining that the rating of the target store laid on the rating dimension reaches the standard; and counting the dimension number of the target store rating reaching the standard.
According to the embodiment, a mode of grading multiple grading dimensions is adopted, the grading standard of each grading dimension is accurately evaluated, and the grading dimension with the standard grading is accurately found, so that the grade of the shop can be accurately obtained.
As to a specific scoring manner, as an exemplary selectable manner of scoring, the calculating, according to the obtained platform data of the target store, scores corresponding to a plurality of rating dimensions of the target store, may specifically include: and if the rating dimension is the first dimension of the local specific user behavior, determining a score corresponding to the first dimension of the target shop according to the number of the consumers of the local specific user in the target shop within a preset time period.
In this embodiment, the local user may be a regular-location user in the same area range as the location information in the store recommendation request, and the local specific user may be a user whose order consumption number is higher than a large-disk turning point value (the overall standard of such local user) in a specific time period (holiday such as weekend) and in a place which is more than a certain distance threshold from the regular-location of the local user. The user population has shop directivity relatively, and tends to be a shop with higher quality. Therefore, the grade corresponding to the grade dimension of the target shop can be accurately determined according to the number of the consumers of the local specific user of the target shop in the preset time period.
As another optional way of scoring, if the rating dimension is the second dimension of the behavior of the foreign user, for the foreign user with the maximum interval of days recorded by orders smaller than or equal to the preset threshold of days (preset according to actual practice), the score corresponding to the target store being laid in the second dimension is determined according to the product of the average consumption of the foreign users of the target store and the number of orders.
The foreign user is determined relative to the local user, i.e., the very-resident user. The shop behavior of the user can reflect the popularity of the shop, so that the rating corresponding to the target shop laid in the rating dimension can be accurately determined according to the number of orders consumed by all people in the shop by the user.
As another optional way of scoring, if the rating dimension is a third dimension of the local user behavior, determining a score corresponding to the third dimension of the target store according to a ratio of the number of local users who consume the store outside the specific range and have a number greater than a preset number threshold to the total number of local users who consume the target store.
The store behavior of the local user can reflect the quality of the store to a certain extent. Therefore, according to the analysis manner of the local user store consumption behavior in the embodiment, the rating corresponding to the target store laid in the rating dimension can be accurately determined.
As still another alternative way of scoring, if the rating dimension is the fourth dimension of all user behaviors, determining a score corresponding to the fourth dimension of the target store according to the product of all orders of the target store and the per-person consumption.
In the embodiment, besides analyzing local users, local specific users and foreign users, comprehensive judgment can be performed according to the behaviors of all the users, and the advantage of the stores in the rating dimension can be accurately judged for all orders and per-person consumption of the stores.
As another optional way of scoring, if the rating dimension is the fifth dimension of the store comment, determining a score corresponding to the fifth dimension of the target store where the target store is laid according to the user comment score of the target store.
The user comment scores of the stores can intuitively indicate the quality of the stores, so that the scores corresponding to the rating dimensions of the target stores can be accurately determined according to the user comment scores of the target stores.
Each rating dimension has a respective threshold value due to the different scoring manner of each rating dimension. Setting a threshold value determines a standard of reaching a rating, so in order to accurately determine whether a rating dimension of a store meets the rating standard, further optionally, before determining, for each rating dimension, whether a score corresponding to a target store is greater than or equal to the threshold value corresponding to the rating dimension, the method of this embodiment may further include: according to the number of store recommendations (such as the number of stores requiring top-ranking recommendations), the ranking distribution (such as the number of recommendations of each store ranking of the top-ranking), and the priority among multiple ranking dimensions (such as the importance of the different ranking dimensions to the store rankings, the priority among the different ranking dimensions is divided, and the higher the priority is, the more important the store ranking determination is), which are within the preset range of the position information in the store recommendation request, the threshold corresponding to each of the multiple ranking dimensions is determined integrally. Through the alternative mode, the most appropriate threshold value can be obtained, namely the requirement of rating reaching judgment can be met, and the recommendation requirement of the shop can also be met.
Illustratively, according to the number of store recommendations within a preset range of the location information in the store recommendation request, the rank distribution, and the priorities among the multiple rating dimensions, the threshold values corresponding to the multiple rating dimensions are integrally determined, which may specifically include: according to the sequence of the priorities among the plurality of rating dimensions from high to low, Z-shaped searching is carried out according to the shop scores of all shops within the preset range of the position information in the shop recommendation request, and the searching is stopped until the number of the searched shops meets the stop condition, wherein the stop condition is determined according to the recommended number of the shops and the grade gear distribution; and determining a threshold value corresponding to each of the plurality of rating dimensions by referring to the last store score searched after the plurality of rating dimensions stop searching.
For example, if a store in the city range needs to be recommended to the user, the top-ranking store data of the city recommendation top-ranking, the top-ranking store gear distribution and the priority among multiple rating dimensions are determined according to the scale of the city. As shown in fig. 2, the algorithm model is searched in a zigzag manner according to the rating dimensions, and when the number of searched shops meets the number of top-ranking shops and the top-ranking shop rank distribution, the threshold corresponding to each of the rating dimensions can be found. By the method, the threshold corresponding to each rating dimension can be accurately determined, and further the threshold can be used as a standard for judging the standard to reach the standard, and the shop grade of the shop can be accurately determined.
Optionally, step 104 may specifically include: obtaining target shops with the shop levels larger than a preset level threshold value, and screening according to the number of shops needing to be recommended in each level gear; and the server returns the screened recommendation information of the target shop to the client.
For example, there are 5 rating dimensions, in each of which the a-level may be regarded as the highest level, and the preset level threshold may be 3A, so that the 4A and 5A stores larger than 3A may be used as recommended stores, and recommended to the user. In practice, there are likely to be many 4A and 5A shops, and the size of the location area displayed for the user is limited, so that each grade can be set in advance, for example, 2 shops recommend for 5A shop and 1 shop recommends for 4A shop, and then the number of shops needing to be recommended according to each grade is screened, and 2 shops for 5A and 1 shop for 4A are screened and recommended to the user.
Compared with the conventional mode, the embodiment recommends the target store information of which the store level meets the preset level condition to the user from the consideration of a plurality of different rating dimensions. On one hand, the accuracy and comprehensiveness of shop information recommendation can be achieved, and the cost of searching the required information by the user is saved; on the other hand, the method can reduce the invasion of the privacy data of the user and protect the privacy of the user.
The content of the foregoing embodiment is an information recommendation process described at a server side, and further, to fully illustrate an implementation of this embodiment, this embodiment further provides another information recommendation method, which is applicable to a client side, as shown in fig. 3, where the method includes:
The store recommendation request carries position information, and further, the server determines, according to platform data of each target store within a preset range of the position information, a store level corresponding to each target store based on a plurality of rating dimensions, wherein the platform data includes: store data of the targeted store, historical order data, and corresponding order user data.
Optionally, after step 202, the method of this embodiment may further include: and the client outputs the received recommendation information of the target stores and the corresponding store grade marks of the target stores.
For example, the recommended store contents may be output in a list based on the received store recommendation information, and the store ranks of the stores, such as the 5A store, the 4A store, and the like, may be marked. In addition, some recommenders can be output, specifically, a target rating dimension with a relatively high priority and a high score exceeding a threshold value can be selected according to the priority of the rating dimension and the score corresponding to the rating dimension, and then the recommenders are generated and correspondingly displayed in the list by referring to the content related to the target rating dimension.
Compared with the conventional method, the information recommendation method provided by the embodiment recommends the target store information of which the store level meets the preset level condition to the user from the consideration of different rating dimensions. On one hand, the accuracy and comprehensiveness of shop information recommendation can be achieved, and the cost of searching the required information by the user is saved; on the other hand, the method can reduce the invasion of the privacy data of the user and protect the privacy of the user.
For convenience of understanding of the specific implementation process of the method in each embodiment, the following application scenarios are given, but not limited to:
in the recommendation demand of restaurant stores, based on the method of the embodiment, 5 dimensions are extracted by processing payment data: the good store standard of the restaurant is redefined from the 5 dimensions by respectively having high local person special journey/visitor popularity/local person return rate/popularity burst/score. I.e. the restaurant was rated from 5 dimensions, a representing the highest rating.
Local eating special journey: if the number of times of consumption of the ordinary resident user in places, such as weekends and holidays, of which the number is more than the inflection point value of the large plate, is defined as local food, and all stores which have local food to store in half a year and the number of people exceeding the threshold value calculated by the algorithm can be evaluated as A-grade of local food special journey.
The visitor awareness: if users who are very resident and have the largest transaction day interval less than or equal to 7 days are ranked from high to low according to the per-person order number, stores which reach a certain threshold value calculated by the algorithm can be evaluated as class A of the popularity of the tourists.
Local human return rate: if the denominator is the local population number consumed by the store, and the numerator is the local population number consumed by the store beyond the range of 800m for more than two times, the store reaching a certain threshold value calculated by the algorithm can be evaluated as the A grade of the local population return rate.
Explosion of the shed by human air: if all orders are ranked from high to low average price, stores that reach some threshold calculated by the algorithm may be rated as a class a of free burst.
The score is high: stores with a score above 4.0 (the algorithm may calculate a threshold, full score may be 5.0) may be rated as a high-scoring class a.
Based on the above 5 dimensions, the embodiment ranks restaurant stores near the user or in a city, and so on, and then recommends the restaurant store whose rank meets the requirement to the user, and a specific display effect can be as shown in fig. 4. By the scheme, accurate recommendation of high-quality catering stores can be achieved, and the cost for searching required information by a user is saved.
Further, as a specific implementation of the method shown in fig. 1, the present embodiment provides an information recommendation apparatus applicable to a service end side, as shown in fig. 5, the apparatus includes: a receiving module 31, an obtaining module 32, a determining module 33, and a sending module 34.
The system comprises a receiving module 31, a position information acquiring module and a position information acquiring module, wherein the receiving module 31 is used for receiving a shop recommendation request which carries position information;
an obtaining module 32, configured to obtain platform data of each target store within a preset range of the location information, where the platform data includes: store data, historical order data and corresponding order user data of the target store;
the determining module 33 is configured to determine, according to the platform data, respective corresponding store levels of the target stores based on the plurality of rating dimensions;
and the sending module 34 is used for returning the recommendation information of the target shop of which the shop grade meets the preset grade condition.
In a specific application scenario, the determining module 33 is specifically configured to obtain, according to the platform data, a dimension number of a target store that reaches a standard in a rating dimension; and determining the shop grade corresponding to the target shop according to the dimension number of the qualified rating.
In a specific application scenario, the determining module 33 is further specifically configured to calculate, according to the platform data, scores corresponding to a plurality of rating dimensions of the target store; for each rating dimension, judging whether the score corresponding to the target store is larger than or equal to the threshold value corresponding to the rating dimension, if so, determining that the rating of the target store laid on the rating dimension reaches the standard; and counting the dimension number of the target store rating reaching the standard.
In a specific application scenario, the determining module 33 is further configured to, before determining, for each rating dimension, whether the score corresponding to the target store is greater than or equal to the threshold corresponding to the rating dimension, integrally determine, according to the recommended number of stores within the preset range of the location information, the rank distribution, and the priorities among the plurality of rating dimensions, the thresholds corresponding to the plurality of rating dimensions.
In a specific application scenario, the determining module 33 is specifically configured to perform a zigzag search according to the store scores of the stores within the preset range of the location information in an order from high to low in priority among the plurality of rating dimensions, and stop the search until the number of searched stores meets a stop condition, where the stop condition is determined according to the recommended number of stores and the distribution of the rating steps; and determining a threshold value corresponding to each of the plurality of rating dimensions by referring to the shop score finally searched after the plurality of rating dimensions are stopped.
In a specific application scenario, the determining module 33 is further configured to determine, if the rating dimension is a first dimension of the local specific user behavior, a rating corresponding to the first dimension of the target store according to the number of consumers of the local specific user of the target store within a preset time period; if the rating dimension is the second dimension of the behavior of the foreign users, determining the corresponding rating of the target store in the second dimension according to the product of the average consumption of the foreign users of the target store and the amount of orders for the foreign users with the maximum interval of days recorded by orders less than or equal to a preset threshold of days; if the rating dimension is a third dimension of local user behaviors, determining a score corresponding to the third dimension of the target store according to the ratio of the number of local users who consume the stores outside the specific range and have the times larger than a preset time threshold value to the total number of local users who consume the target store; if the rating dimension is the fourth dimension of all user behaviors, determining the score of the target store corresponding to the fourth dimension according to the product of all orders of the target store and the per-person consumption; and if the rating dimension is the fifth dimension of the store comment, determining the grade of the target store corresponding to the fifth dimension according to the user comment grade of the target store.
In a specific application scenario, the sending module 34 is specifically configured to obtain a target store of which the store level is greater than a preset level threshold, and perform screening according to the number of stores that need to be recommended in each level; and returning the recommendation information of the screened target shop.
It should be noted that other corresponding descriptions of the functional units related to the information recommendation device applicable to the server side provided in this embodiment may refer to the corresponding description of the method in fig. 1, and are not described herein again.
Further, as a specific implementation of the method shown in fig. 3, an embodiment of the present application provides an information recommendation apparatus applicable to a client side, as shown in fig. 6, the apparatus includes: a sending module 41 and a receiving module 42.
A sending module 41, configured to send a store recommendation request, where the store recommendation request carries location information, so that according to platform data of each target store within a preset range of the location information, a store level corresponding to each target store is determined based on multiple rating dimensions, where the platform data includes: store data, historical order data and corresponding order user data of the target store;
and the receiving module 42 is configured to receive recommendation information of a target store of which the store level meets a preset level condition.
In a specific application scenario, the apparatus further comprises: an output module;
and the output module is used for outputting the received recommendation information of the target shop and the shop grade marks corresponding to the target shops respectively after receiving the recommendation information of the target shop of which the shop grade meets the preset grade condition.
It should be noted that other corresponding descriptions of the functional units related to the information recommendation device applicable to the client side provided in this embodiment may refer to the corresponding description of the method in fig. 3, and are not described herein again.
Based on the method shown in fig. 1, correspondingly, the present application further provides a storage medium, on which a computer program is stored, and the computer program, when executed by a processor, implements the method shown in fig. 1. Based on the method shown in fig. 3, the present application further provides another storage medium, on which a computer program is stored, and the computer program, when executed by a processor, implements the method shown in fig. 3.
Based on such understanding, the technical solution of the present application may be embodied in the form of a software product, which may be stored in a non-volatile storage medium (which may be a CD-ROM, a usb disk, a removable hard disk, etc.), and includes several instructions for enabling a computer device (which may be a personal computer, a server, or a network device, etc.) to execute the method of the embodiments of the present application.
Based on the method shown in fig. 1 and the virtual device embodiment shown in fig. 5, in order to achieve the above object, the present application embodiment further provides a server device, which may specifically be a server, a computer device, or other network devices. The client device includes a storage medium and a processor; a storage medium for storing a computer program; a processor for executing a computer program for implementing the above-described method as shown in fig. 1.
Based on the method shown in fig. 3 and the virtual device embodiment shown in fig. 6, in order to achieve the above object, an embodiment of the present application further provides a client device, which may specifically be a tablet computer, a smart phone, a smart watch, a smart bracelet, or other network devices. The apparatus includes a storage medium and a processor; a storage medium for storing a computer program; a processor for executing a computer program for implementing the above-described method as shown in fig. 3.
Optionally, both the two entity devices may further include a user interface, a network interface, a camera, a Radio Frequency (RF) circuit, a sensor, an audio circuit, a WI-FI module, and the like. The user interface may include a Display screen (Display), an input unit such as a keypad (Keyboard), etc., and the optional user interface may also include a USB interface, a card reader interface, etc. The network interface may optionally include a standard wired interface, a wireless interface (e.g., WI-FI interface), etc.
Those skilled in the art will appreciate that the physical device structure of the client device and the server device provided in the present embodiment does not constitute a limitation to the two physical devices, and may include more or less components, or combine some components, or arrange different components.
The storage medium may further include an operating system and a network communication module. The operating system is a program that manages the hardware and software resources of the two physical devices described above, supporting the operation of the information processing program as well as other software and/or programs. The network communication module is used for realizing communication among components in the storage medium and communication with other hardware and software in the information processing entity device.
Based on the above, further, an embodiment of the present application further provides an information recommendation system, where the system includes a server device and a client device.
Wherein the server device is operable to perform the method as shown in fig. 1 and the client device is operable to perform the method as shown in fig. 3.
The system comprises a client device and a server device, wherein the client device is used for sending a shop recommendation request to the server device, and the shop recommendation request carries position information.
The server equipment is used for receiving the shop recommendation request sent by the client equipment; acquiring platform data of each target store in a preset range of position information carried in the store recommendation request, wherein the platform data comprises: store data, historical order data and corresponding order user data of the target store; then according to the platform data, determining the corresponding shop grade of the target shop based on a plurality of rating dimensions; and finally, returning the recommendation information of the target shop of which the shop grade meets the preset grade condition to the client equipment.
And the client equipment is used for receiving the recommendation information of the target shop of which the shop grade meets the preset grade condition, returned by the server equipment.
Through the above description of the embodiments, those skilled in the art will clearly understand that the present application can be implemented by software plus a necessary general hardware platform, and can also be implemented by hardware. By applying the technical scheme of the embodiment, compared with the conventional mode, the embodiment does not need to rely on information browsing records of users for store information recommendation, and the reference data adopted by the embodiment is platform data of each target store in a position range, and specifically comprises store data of the target store, historical order data and corresponding order user data. And determining the corresponding shop grade of the target shop based on the plurality of rating dimensions according to the reference data, and recommending the target shop information of which the shop grade meets the preset grade condition to the user in consideration of different rating dimensions. On one hand, the accuracy and comprehensiveness of shop information recommendation can be achieved, and the cost of searching the required information by the user is saved; on the other hand, the method can reduce the invasion of the privacy data of the user and protect the privacy of the user.
Those skilled in the art will appreciate that the figures are merely schematic representations of one preferred implementation scenario and that the blocks or flow diagrams in the figures are not necessarily required to practice the present application. Those skilled in the art will appreciate that the modules in the devices in the implementation scenario may be distributed in the devices in the implementation scenario according to the description of the implementation scenario, or may be located in one or more devices different from the present implementation scenario with corresponding changes. The modules of the implementation scenario may be combined into one module, or may be further split into a plurality of sub-modules.
The above application serial numbers are for description purposes only and do not represent the superiority or inferiority of the implementation scenarios. The above disclosure is only a few specific implementation scenarios of the present application, but the present application is not limited thereto, and any variations that can be made by those skilled in the art are intended to fall within the scope of the present application.
Claims (10)
1. An information recommendation method, comprising:
receiving a shop recommendation request, wherein the shop recommendation request carries position information;
acquiring platform data of each target shop within a preset range of the position information, wherein the platform data comprises: store data, historical order data and corresponding order user data of the target store;
determining the respective corresponding shop grades of the target shops based on a plurality of rating dimensions according to the platform data;
and returning the recommendation information of the target shop of which the shop grade meets the preset grade condition.
2. The method as claimed in claim 1, wherein the determining, according to the platform data, the respective store grades corresponding to the target stores based on a plurality of rating dimensions specifically comprises:
obtaining the dimension number of the target shop reaching the rating standard in a plurality of rating dimensions according to the platform data;
and determining the shop grade corresponding to the target shop according to the dimension number of the qualified rating.
3. The method according to claim 2, wherein the obtaining, according to the platform data, the number of dimensions of the target store that meet the rating standards in a plurality of rating dimensions specifically comprises:
calculating scores corresponding to a plurality of rating dimensions of target shops according to the platform data;
for each rating dimension, judging whether the score corresponding to the target store is larger than or equal to the threshold value corresponding to the rating dimension, if so, determining that the rating of the target store laid on the rating dimension reaches the standard;
and counting the dimension number of the target store rating reaching the standard.
4. The method of claim 3, wherein before determining, for each rating dimension, whether the score corresponding to the target store is greater than or equal to the threshold corresponding to the rating dimension, the method further comprises:
and integrally determining the threshold values corresponding to the plurality of rating dimensions according to the recommended number of stores, the grade gear distribution and the priorities among the plurality of rating dimensions within the preset range of the position information.
5. The method according to claim 4, wherein the integrally determining the threshold values corresponding to the plurality of rating dimensions according to the recommended number of stores, the level-shift distribution and the priorities among the plurality of rating dimensions within the preset range of the location information specifically comprises:
according to the sequence of the priorities among the plurality of rating dimensions from high to low, Z-shaped searching is carried out according to the shop scores of all shops within the preset range of the position information, and the searching is stopped until the number of the searched shops meets the stop condition, wherein the stop condition is determined according to the recommended number of the shops and the grade gear distribution;
and determining a threshold value corresponding to each of the plurality of rating dimensions by referring to the shop score finally searched after the plurality of rating dimensions are stopped.
6. An information recommendation method, comprising:
sending a shop recommendation request, wherein the shop recommendation request carries position information, so that according to platform data of each target shop within a preset range of the position information, the respective corresponding shop level of the target shop is determined based on a plurality of rating dimensions, and the platform data comprises: store data, historical order data and corresponding order user data of the target store;
and receiving recommendation information of the target shop of which the shop grade meets the preset grade condition.
7. An information recommendation apparatus, comprising:
the system comprises a receiving module, a processing module and a display module, wherein the receiving module is used for receiving a shop recommendation request which carries position information;
the acquisition module is used for acquiring platform data of each target shop within a preset range of the position information, wherein the platform data comprise: store data, historical order data and corresponding order user data of the target store;
the determining module is used for determining the corresponding shop grade of the target shop based on a plurality of rating dimensions according to the platform data;
and the sending module is used for returning the recommendation information of the target shop of which the shop grade meets the preset grade condition.
8. An information recommendation apparatus, comprising:
the system comprises a sending module and a receiving module, wherein the sending module is used for sending a shop recommendation request, and the shop recommendation request carries position information, so that the respective corresponding shop levels of target shops are determined based on a plurality of rating dimensions according to platform data of each target shop within a preset range of the position information, and the platform data comprises: store data, historical order data and corresponding order user data of the target store;
the receiving module is used for receiving the recommendation information of the target shop of which the shop grade meets the preset grade condition.
9. A storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the method of any of claims 1 to 6.
10. An electronic device comprising a storage medium, a processor and a computer program stored on the storage medium and executable on the processor, wherein the processor implements the method of any one of claims 1 to 6 when executing the computer program.
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| Application Number | Priority Date | Filing Date | Title |
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| CN202110639366.3A CN113360790A (en) | 2021-06-08 | 2021-06-08 | Information recommendation method and device and electronic equipment |
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| CN202110639366.3A CN113360790A (en) | 2021-06-08 | 2021-06-08 | Information recommendation method and device and electronic equipment |
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114301973A (en) * | 2021-12-24 | 2022-04-08 | 支付宝(杭州)信息技术有限公司 | Information recommendation processing method and device |
| CN116561769A (en) * | 2023-05-19 | 2023-08-08 | 国家计算机网络与信息安全管理中心 | Vendor recommendation method, device, equipment and storage medium |
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2021
- 2021-06-08 CN CN202110639366.3A patent/CN113360790A/en active Pending
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114301973A (en) * | 2021-12-24 | 2022-04-08 | 支付宝(杭州)信息技术有限公司 | Information recommendation processing method and device |
| CN116561769A (en) * | 2023-05-19 | 2023-08-08 | 国家计算机网络与信息安全管理中心 | Vendor recommendation method, device, equipment and storage medium |
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