CN113569133A - Information recommendation method and device - Google Patents
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Abstract
The application discloses an information recommendation method and device. The method comprises the steps of firstly, determining an alternative recommendation list according to the positioning of a user; and then, analyzing the personal preference of the user by acquiring historical behavior data associated with the user and the energy supply stations, and performing secondary screening and sequencing on the energy supply stations in the alternative recommendation list according to the personal preference of the user to obtain a recommendation list more conforming to the personal preference of the user. Therefore, the source supply station recommendation is carried out by using the recommendation list which is more in line with the personal preference of the user, the user can be helped to find the preferred energy supply station more quickly, and the personalized customization recommendation of the energy supply station is realized.
Description
Technical Field
The present application relates to the field of communications technologies, and in particular, to an information recommendation method and apparatus.
Background
When brand or place search is performed on most application programs (APPs) nowadays, besides providing an input box of search keywords, historical search records, popular recommendations, candidate options sorted by letters and the like are displayed on a search interface.
The hot recommendations are usually based on search records of users in the whole network, and the hot recommendations cannot necessarily meet specific requirements of specific users; letter sorting is a relatively universal retrieval method, and certain convenience can be provided only when a user definitely knows a target brand and a target place which the user wants to search; although the history search record can record the search once performed by the user, the function is closer to the bookmark function, and certain convenience can be provided only when the user wants to perform the same search again.
In fact, due to different preferences of charging brands, different activity ranges and different frequent charging periods, the real needs of each user can be more or less different individually, but existing application programs do not realize personalized customized recommendation in a real sense when searching for brands or places.
Disclosure of Invention
The applicant creatively provides an information recommendation method and device.
According to a first aspect of embodiments of the present application, there is provided an information recommendation method, including: determining a first recommendation list of the energy supply station according to the positioning of the user; acquiring historical behavior data associated with a user and an energy supply station; determining a personal preference score for each energy supply station in the first recommendation list based on the historical behavior data; and screening and sorting the energy supply stations in the first recommendation list according to the personal preference scores to obtain a second recommendation list.
According to an embodiment of the application, determining the personal preference score of each energy supply station in the first recommendation list according to the historical behavior data comprises: acquiring the selection reasonableness of each energy supply station in the first recommendation list according to historical behavior data, wherein the selection reasonableness represents the difference degree between the predicted selection result and the actual selection result, and the selection reasonableness is lower when the difference degree is larger; and determining the personal preference score of each energy supply station in the first recommendation list according to the selection reasonableness, so that the lower the selection reasonableness, the higher the personal preference score.
According to an embodiment of the application, determining the personal preference score of each energy supply station in the first recommendation list according to the historical behavior data comprises: acquiring a historical waiting time threshold of a user according to historical behavior data; acquiring the waiting time of each energy supply station in the first recommendation list in the current time period; and determining the personal preference score of each energy supply station in the first recommendation list according to the waiting time and the historical waiting time threshold.
According to an embodiment of the application, determining the personal preference score of each energy supply station in the first recommendation list according to the historical behavior data comprises: determining a suspected destination of the user according to a destination corresponding to the current time period in the historical behavior data; acquiring the distance between each energy supply station in the first recommendation list and a suspected destination; and determining the personal preference score of each energy supply station in the first recommendation list according to the distance from the suspected destination.
According to an embodiment of the present application, determining a suspected destination of a user according to a destination corresponding to a current time period in historical behavior data includes: if the current time interval is the working time interval, determining the company address of the user as the suspected destination of the user; and if the current time interval is the off-duty time interval, determining the home address of the user as the suspected destination of the user.
According to an embodiment of the application, determining the personal preference score of each energy supply station in the first recommendation list according to the historical behavior data comprises: acquiring the access frequency of a user to each energy supply station in the first recommendation list according to historical behavior data; and determining the personal preference score of each energy supply station in the first recommendation list according to the access frequency.
According to an embodiment of the application, determining the personal preference score of each energy supply station in the first recommendation list according to the historical behavior data comprises: acquiring selection factor preference of a user according to historical behavior data; a personal preference score for each energy delivery station in the first recommendation list is determined based on the selection factor preferences.
According to an embodiment of the application, determining the personal preference score of each energy supply station in the first recommendation list according to the historical behavior data comprises: obtaining an evaluation record of each energy supply station in a first recommendation list according to the evaluation record of the energy supply station in the historical behavior data; and determining the personal preference score of each energy supply station in the first recommendation list according to the evaluation records.
According to an embodiment of the application, determining the personal preference score of each energy supply station in the first recommendation list according to the historical behavior data comprises: according to at least two kinds of data in the historical behavior data, scoring each energy supply station in the first recommendation list respectively to obtain at least two scoring results; and weighting and summing at least two scoring results to obtain the personal preference score of each energy supply station.
According to an embodiment of the application, determining the personal preference score of each energy supply station in the first recommendation list according to the historical behavior data comprises: and determining the personal preference score of each energy supply station in the first recommendation list according to the historical behavior data and the user behavior machine learning model.
According to an embodiment of the application, after obtaining the second recommendation list, the method further includes: acquiring personal information and vehicle information of a user; and adjusting the personal preference scores of the second recommendation list according to the personal information and the vehicle information to obtain the adjusted personal preference scores.
According to an embodiment of the present application, acquiring personal information of a user includes: acquiring personal portrait information of a user; mapping the user to a corresponding user group according to the personal portrait information to obtain a first user group; correspondingly, the adjusting the personal preference score of the second recommendation list according to the personal information and the vehicle information to obtain the adjusted personal preference score comprises the following steps: and adjusting the personal preference scores of the second recommendation list according to the first user group preference and the vehicle information to obtain the adjusted personal preference scores.
According to the above-described embodiment of the present application, the historical behavior data includes at least one of a positioning record, an energy supply station search record, an energy supply station navigation record, and an energy supply station evaluation record.
According to a second aspect of the embodiments of the present application, there is provided an information recommendation apparatus, including: the first recommendation list determining module is used for acquiring historical behavior data of the user related to the energy supply station; the historical behavior data acquisition module is used for acquiring historical behavior data associated with the energy supply station by the user; the personal preference score determining module is used for determining the personal preference score of each energy supply station in the first recommendation list according to the historical behavior data; and the personalized screening and sorting module is used for screening and sorting the energy supply stations in the energy supply station recommendation list according to the personal preference scores to obtain a second recommendation list.
The embodiment of the application provides an information recommendation method and device, and the method comprises the steps of firstly determining an alternative recommendation list according to the positioning of a user; and then, analyzing the personal preference of the user by acquiring historical behavior data associated with the user and the energy supply stations, and performing secondary screening and sequencing on the energy supply stations in the alternative recommendation list according to the personal preference of the user to obtain a recommendation list more conforming to the personal preference of the user. Therefore, the source supply station recommendation is carried out by using the recommendation list which is more in line with the personal preference of the user, the user can be helped to find the preferred energy supply station more quickly, and the personalized customization recommendation of the energy supply station is realized.
It is to be understood that the implementation of the present application does not require all of the above-described advantages to be achieved, but rather that certain technical solutions may achieve certain technical effects, and that other embodiments of the present application may also achieve other advantages not mentioned above.
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The above and other objects, features and advantages of exemplary embodiments of the present application will become readily apparent from the following detailed description read in conjunction with the accompanying drawings. Several embodiments of the present application are illustrated by way of example, and not by way of limitation, in the figures of the accompanying drawings and in which:
in the drawings, the same or corresponding reference numerals indicate the same or corresponding parts.
Fig. 1 is a schematic view of an implementation flow of an embodiment of an information recommendation method of the present application;
fig. 2 is a schematic view of an implementation flow of another embodiment of the information recommendation method of the present application;
fig. 3 is a schematic structural diagram of an embodiment of an information recommendation device according to the present application.
Detailed Description
In order to make the objects, features and advantages of the present application more obvious and understandable, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is apparent that the described embodiments are only a part of the embodiments of the present application, and not all the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
In the description herein, reference to the description of the term "one embodiment," "some embodiments," "an example," "a specific example," or "some examples," etc., means that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the application. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples. Furthermore, various embodiments or examples and features of different embodiments or examples described in this specification can be combined and combined by one skilled in the art without contradiction.
Furthermore, the terms "first", "second" and "first" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one such feature. In the description of the present application, "a plurality" means two or more unless specifically limited otherwise.
Fig. 1 shows an implementation flow of an embodiment of an information recommendation method of the present application. Referring to fig. 1, the method includes: an operation 110 of determining a first recommendation list of energy supply stations according to the location of the user; an operation 120 of obtaining historical behavior data associated with the energy supply station by the user; an operation 130 of determining a personal preference score for each energy supply station in the first recommendation list based on the historical behavior data; at operation 140, the energy supply stations in the first recommendation list are filtered and sorted according to the personal preference scores to obtain a second recommendation list.
In this embodiment, the information recommendation method is mainly applied to a search or recommendation system or Application (APP) of an energy supply site.
Wherein, the energy supply station mainly refers to stations or shops such as gas stations, gas filling stations, charging piles and the like.
When determining the first recommendation list of the energy supply station according to the location of the user, in operation 110, any suitable recommendation method in the existing schemes may be used to obtain the corresponding recommendation list, for example, a historical recommendation list determined according to the user search record or navigation record of the energy supply station; selecting a determined hotspot recommendation list according to most users; determining high-quality energy supply station recommendation according to user evaluation; determining a new energy supply station list according to the energy supply station which is newly opened; alphabetically sorted recommendation lists or recommendation lists derived from keywords entered by the user, etc.
The distance ranges of the sites in these recommendation lists from the current position may be determined by the user, e.g., <500m, <1000m, and over 1000m, etc.
Each option in the recommendation list may provide, in addition to the name and geographical location of the energy supply station, information on the distance of the energy supply station from the current location, the time expected to arrive at the energy supply station, the time required to wait in line after arriving at the energy supply station, and the like.
In operation 120, historical behavior data of the user associated with the energy supply station is obtained, and may be obtained from various records in the system or the application, such as a user positioning history, an energy supply station search history, an energy supply station navigation history, a target object evaluation history, and the like; the user location information history, energy supply station search history, fueling information history, charging information history, and the like may also be obtained from other third party systems or applications installed by the user according to a certain protocol.
Typically, such historical behavior data associated with the energy supply stations is useful in analyzing user preference historical behavior data and is formed by recording and storing the user's relevant behavior data each time.
After obtaining such historical behavior data that facilitates analyzing the user's preferences via operation 110, the user's preferences may be analyzed based on the historical behavior data via operation 130 to determine a personal preference score for each energy delivery station in the first recommendation list.
In operation 130, the personal preferences in this application refer specifically to the user's personal preferences in selecting energy supply stations, e.g., preferences for energy supply station brands; a preference for energy brands; preference for selection factors for the energy supply station (e.g., preference for close distance, preference for short wait time, or preference for more preference), etc.
When determining the personal preference score of the energy supply station, the score is often made according to the degree of conformity of the energy supply station with the personal preference of the user, and the score is higher according to the personal preference of the user. The score may be a predefined, discrete scale; or calculating according to a certain calculation formula to obtain the score; or a score determined from the output of some comprehensive scoring model.
After the personal preference score for each energy supply station in the first recommendation list is obtained in operation 130, the energy supply stations in the first recommendation list are filtered and sorted according to the personal preference score to obtain a second recommendation list in operation 140.
In operation 140, a filtering may be performed to remove energy supply stations with personal preference scores less than a personal preference score threshold from the first recommendation list; or the sorting can be carried out firstly, and only N energy supply stations which are the first to be sorted are selected. Wherein the human preference score threshold or the N value can be set by a default configuration of the system or by user interaction.
Therefore, in the information recommendation method provided by the embodiment, firstly, an alternative recommendation list is determined according to the positioning of the user through operation 110; thereafter, historical behavior data of the user associated with the energy supply station is obtained through operation 120; such that, subsequently, in operation 130, the personal preferences of the user are analyzed based on the historical behavioral data to determine a personal preference score for each energy delivery station in the first recommendation list; then, through operation 140, the energy supply stations in the candidate recommendation list are secondarily filtered and sorted according to the personal preference score of each energy supply station, so as to obtain a recommendation list more suitable for the personal preference of the user.
Therefore, the source supply station recommendation is carried out by using the recommendation list which is more in line with the personal preference of the user, the user can be helped to find the preferred energy supply station more quickly, and the personalized customization recommendation of the energy supply station is realized.
Furthermore, as the historical behavior data of the user can be continuously enriched and changed along with the time, the current behavior preference of the user can be more accurately reflected and the change of the preference of the user can be reflected in time. Therefore, the information recommendation method is dynamic and variable personalized customization recommendation, can better conform to the current preference of the user and can change along with the change of the preference of the user.
It should be noted that the embodiment shown in fig. 1 is only one of the most basic embodiments of the information recommendation method of the present application, and further refinements and extensions may be made on the basis of the embodiment.
According to an embodiment of the application, determining the personal preference score of each energy supply station in the first recommendation list according to the historical behavior data comprises: acquiring the selection reasonableness of each energy supply station in the first recommendation list according to historical behavior data, wherein the selection reasonableness represents the difference degree between the predicted selection result and the actual selection result, and the selection reasonableness is lower when the difference degree is larger; and determining the personal preference score of each energy supply station in the first recommendation list according to the selection reasonableness, so that the lower the selection reasonableness, the higher the personal preference score.
In this embodiment, each time a user searches for an energy supply station, the user location, the energy supply station search result corresponding to the user location, and the distance information and waiting time of each energy supply station from the user location at that time in the search result may be recorded and stored to form the user historical behavior data.
And then, when responding to a user request for personalized recommendation, acquiring the stored historical behavior data of the user, and acquiring the following data according to the current positioning of the user: 1) searching results corresponding to the positioning records close to the current positioning and the distance between each energy supply station in the searching results and the current positioning of the user; 2) from the searched navigation record (or energy replenishment record), the source supply station that the user finally selected can be determined.
If the analysis of 1) and 2) is carried out, it is found that: when multiple energy supply stations are available, the user does not select the closest energy supply station a1, but selects a more distant energy supply station B1, indicating that the owner has a preference for the more distant energy supply station B1. Therefore, a higher personal preference score may be set for the more distant energy supply station B1. For example, if the distance from the energy supply station B1 to the anchor point is 900m and the distance from the energy supply station a1 to the anchor point is 600m, the personal preference score of the energy supply station B1 is ((900m-600m)/100m) is 3. Accordingly, a lower personal preference score may also be set for the nearest energy supply station A1, such as exactly the opposite-3 of energy supply station B1.
Similarly, if the user selects a more distant energy supply station B2 instead of the energy supply station a2 with shorter waiting time, the owner of the vehicle prefers the more distant energy supply station B2; if the user selects a lower-scoring energy supply station B3 instead of the higher-scoring energy supply station a3, the owner of the vehicle prefers the farther energy supply station B3, and so on, under the same distance and similar waiting time.
According to an embodiment of the application, determining the personal preference score of each energy supply station in the first recommendation list according to the historical behavior data comprises: acquiring a historical waiting time threshold of a user according to historical behavior data; acquiring the waiting time of each energy supply station in the first recommendation list in the current time period; and determining the personal preference score of each energy supply station in the first recommendation list according to the waiting time and the historical waiting time threshold.
In this embodiment, the history of acceptable waiting time may be recorded and stored every time the user searches for an energy supply station, the waiting time corresponding to the energy supply station selected by the user being each time the user makes an acceptance.
Then, when the personalized recommendation is performed in response to the user request, the longest waiting time (or average waiting time) in the waiting time history record can be obtained as the historical waiting time threshold of the user, and the personal preference score of each energy supply station in the first recommendation list is determined according to the waiting time of each energy supply station in the current period and the historical waiting time threshold.
For example, assuming the historical maximum wait time is used as the historical wait time threshold of the user, the personal preference score of each energy supply station is the difference between the historical maximum wait time and the wait time of the current period. When the longest waiting time of the user history is 30 minutes, the first recommendation list
Waiting time of 10 minutes for the current period of medium energy supply station A4 and energy supply station B4
The waiting time for the current period was 40 minutes, the personal preference score of the energy supply station a4 was 20, and the personal preference score of the energy supply station B4 was-10.
According to an embodiment of the application, determining the personal preference score of each energy supply station in the first recommendation list according to the historical behavior data comprises: determining a suspected destination of the user according to a destination corresponding to the current time period in the historical behavior data; acquiring the distance between each energy supply station in the first recommendation list and a suspected destination; and determining the personal preference score of each energy supply station in the first recommendation list according to the distance from the suspected destination.
In this embodiment, it is possible to presume the destination of the user each time the user searches for an energy supply station, record and store the user location, time, and presumed destination, and form a destination history corresponding to the location and time period.
And then, when responding to the request of the user to carry out personalized recommendation, acquiring a destination historical record corresponding to the positioning and the time period. If the historical behavior data is found by combining the historical navigation record of the user going out in the time period in the third-party system or application: if the user prefers to select energy supply stations near the destination for energy supply instead of selecting energy supply stations near the current location for energy supply in this period, the destination in the historical behavior data may be acquired as a suspected destination, and the personal preference score of each energy supply station in the first recommendation list may be scored according to the distance between each energy supply station in the first recommendation list and the suspected destination, so that the personal preference score of the energy supply station closer to the suspected destination is higher.
According to an embodiment of the present application, determining a suspected destination of a user according to a destination corresponding to a current time period in historical behavior data includes: if the current time interval is the working time interval, determining the company address of the user as the suspected destination of the user; and if the current time interval is the off-duty time interval, determining the home address of the user as the suspected destination of the user.
Typically, users are on the way to the company during work hours, and on the way to home during work hours. Therefore, in this embodiment, the company address is directly determined as the suspected destination of the working hours, and the home address is determined as the suspected destination of the working hours. Wherein, the company address and the family address can be input by the user; or may be determined from previous historical behavioral data; or may be acquired in a third party system or application.
According to an embodiment of the application, determining the personal preference score of each energy supply station in the first recommendation list according to the historical behavior data comprises: acquiring the access frequency of a user to each energy supply station in the first recommendation list according to historical behavior data; and determining the personal preference score of each energy supply station in the first recommendation list according to the access frequency.
In this embodiment, the energy supply station selected by the user finally may be recorded and stored each time the user searches for an energy supply station, and a history of selection of energy supply stations by the user may be formed.
And then, when responding to a user request for personalized recommendation, acquiring the stored energy supply station historical selection record corresponding to the user positioning, counting the access frequency of the user to each energy supply station in the first recommendation list, and determining a personal preference score according to the access frequency, wherein the higher the access frequency is, the higher the personal preference score is.
According to an embodiment of the application, determining the personal preference score of each energy supply station in the first recommendation list according to the historical behavior data comprises: acquiring selection factor preference of a user according to historical behavior data; a personal preference score for each energy delivery station in the first recommendation list is determined based on the selection factor preferences.
In this embodiment, the selection factor of the energy supply station selected by the user may be presumed each time the user searches for the energy supply station, and then the selection factor of each time the user is recorded and stored to form the selection factor history.
Wherein the selection factors of the user are preset by the system and generally correspond to the sorting factors, such as "distance", "waiting time", "user evaluation" and "brand", etc.
When the selection factor of the energy supply station is estimated by the user, the energy supply station may be determined based on the ranking of the energy supply station in the search results under various "selection factors". For example, if the energy supply station selected by the user at this time is closest, the selection factor of the user at this time is "distance"; if the energy supply station selected by the user at this time is the one with the shortest waiting time, the selection factor of the user at this time is "time"; if the energy supply station selected by the user at this time is the highest user evaluation, the selection factor of the user at this time is 'user evaluation'; if the energy supply station selected by the user at this time is a brand that is frequently selected, the selection factor of the user at this time is "brand".
When the selection factor of the energy supply station selected by the user is estimated, the search result and the energy supply account selected by the user may be input into a selection factor determination model established in advance, and the selection factor of the energy supply station may be acquired through output of the model.
In addition, the sorting factor selected by the user can be recorded and stored as the selection factor of the user each time the user clicks the sorting factor.
And then, when responding to the request of the user to carry out personalized recommendation, acquiring the historical record of the selection factors, and selecting the selection factor with the largest occurrence frequency as the ranking factor of the recommendation.
According to an embodiment of the application, determining the personal preference score of each energy supply station in the first recommendation list according to the historical behavior data comprises: obtaining an evaluation record of each energy supply station in a first recommendation list according to the evaluation record of the energy supply station in the historical behavior data; and determining the personal preference score of each energy supply station in the first recommendation list according to the evaluation records.
In this embodiment, the energy supply station evaluation record may be formed by recording and storing the user's evaluation of the energy supply station each time the energy supply station is evaluated.
Then, when the personalized recommendation is carried out in response to the user request, the evaluation record of the user on the energy supply stations can be obtained, and the personal preference score of each energy supply station can be determined according to the evaluation record. For example, if the energy supply station has a corresponding evaluation record, the score in the evaluation of the energy attack station of the user is directly used as the personal preference score; and if no corresponding evaluation record exists, using the per-person score of the energy supply station as the personal preference score.
According to an embodiment of the application, determining the personal preference score of each energy supply station in the first recommendation list according to the historical behavior data comprises: according to at least two kinds of data in the historical behavior data, scoring each energy supply station in the first recommendation list respectively to obtain at least two scoring results; and weighting and summing at least two scoring results to obtain the personal preference score of each energy supply station.
In the embodiment, when the personal preference score of the energy supply station is determined not according to a certain historical behavior record or a certain scoring factor but according to a plurality of historical behavior records or a plurality of scoring factors, the personal preference score obtained in the way can be integrated with various preference factors and can correct the possible deviation, so that the personal preference score is more comprehensive and accurate.
For example, when there are multiple related records in the user historical behavior record, weighting may be performed according to the distance from the current time, and the weighting is heavier the closer the time is; when a plurality of scoring results are obtained by considering a plurality of selection factors, the frequency of selection of the selection factors by the user can be considered as the weight of the corresponding scoring, and the like.
In addition, the score of one preference factor can be used as the base individual preference score, and the score of another preference factor can be used as a weight for addition.
For example, a personal preference score determined according to the selection reasonableness is used as a base personal preference score, the historical selection frequencies are used as weights to be added, and the like.
According to an embodiment of the application, determining the personal preference score of each energy supply station in the first recommendation list according to the historical behavior data comprises: and determining the personal preference score of each energy supply station in the first recommendation list according to the historical behavior data and the user behavior machine learning model.
In this embodiment, the model is machine learned by basing historical behavior data and user behavior. The implementer may choose any suitable machine learning model and train it using each user's historical behavioral data to arrive at the user's personal preference model.
And then, when responding to the user request for personalized recommendation, directly inputting the user positioning and the current time period to obtain a corresponding recommendation list. Because the newly generated historical behavior data of the user and the energy supply station finally selected by the user can also be used as training data to continuously train the model, the precision of the model can be continuously improved along with the passage of time, and the provided recommendation list can be more and more in line with the real preference of the user.
According to an embodiment of the application, after obtaining the second recommendation list, the method further includes: acquiring personal information and vehicle information of a user; and adjusting the personal preference scores of the second recommendation list according to the personal information and the vehicle information to obtain the adjusted personal preference scores.
The preferences of users of different age groups and different genders for the brand of energy supply station may vary.
Further, with the same ranking, the user may prefer to go to the same brand of energy supply station as the driven vehicle.
In this embodiment, the personal information of the user may be input interactively by the user, or may be acquired from a third-party system or application through a protocol.
According to an embodiment of the present application, acquiring personal information of a user includes: acquiring personal portrait information of a user; mapping the user to a corresponding user group according to the personal portrait information to obtain a first user group; correspondingly, the adjusting the personal preference score of the second recommendation list according to the personal information and the vehicle information to obtain the adjusted personal preference score comprises the following steps: and adjusting the personal preference scores of the second recommendation list according to the first user group preference and the vehicle information to obtain the adjusted personal preference scores.
In this embodiment, the users are classified by acquiring personal portrait information of the users, and classified into a certain user group, for example, a brand loyalty person, a time efficiency priority person, an economic benefit priority person, and then the personal preference scores of the second recommendation list are adjusted according to typical characteristics of such a group to obtain adjusted personal preference scores. The obtained personal preference of the user is more accurate, and the recommendation list sorted according to the adjusted personal preference score can be correspondingly more suitable for the personality and personal habits of the user.
The personal portrait information may be formed from historical behavioral data of the user, or may be obtained from a third party system or application via a protocol.
According to the above embodiments of the present application, the historical behavior data of the user may be different, but generally includes at least one of a positioning record, an energy supply station search record, an energy supply station navigation record and an energy supply station evaluation record.
It should be noted that the above implementation manner of the embodiment of the present application is only an exemplary illustration of the refinement and expansion performed on the basic embodiment shown in fig. 1. The above embodiments may be flexibly combined into new embodiments by the implementers according to specific implementation requirements and implementation conditions.
Fig. 2 shows another embodiment of the information recommendation method of the present application. The embodiment is applied to a new energy automobile charging station recommendation system. The system enters or reduces basic information of a user (for example, age, sex, company address and home address) and brand and model information of a vehicle when user registration or account maintenance is performed. When a user searches for the accessory charging pile each time, the system can record the positioning, the searching time, the searching keywords, the searching sequencing factors, the access record of the charging pile information and the charging pile navigation record of the user. In addition, after each search is finished, the system can also presume the charging pile finally selected by the user according to the charging pile navigation record, and presume the selection factors of the user according to the charging pile and the search result. And finally, storing the finally selected charging piles and the selection factors as historical behavior records of the user.
The system provides a personalized customized recommendation option, and after a user clicks the option, the system mainly executes the following steps to obtain a charging pile recommendation list and displays the charging pile recommendation list in a user interface:
and step 2120, setting the sequence of the charging piles to be the first order, and sequentially moving the other charging piles backwards according to the original sequence to finish the execution.
It should be noted that the embodiment shown in fig. 2 is only an exemplary illustration of the cell handover method of the present application, and is not limited to the embodiment and the application scenario of the cell handover method of the present application. The implementer can adopt any applicable implementation mode and be applied to any applicable application scene according to specific implementation conditions.
Further, the embodiment of the application also provides an information recommendation device. As shown in fig. 3, the apparatus 30 includes: a first recommendation list determining module 301, configured to obtain historical behavior data of a user associated with an energy supply station; a historical behavior data obtaining module 302, configured to obtain historical behavior data of a user associated with an energy supply station; a personal preference score determining module 303, configured to determine a personal preference score of each energy supply station in the first recommendation list according to the historical behavior data; and the personalized screening and sorting module 304 is used for screening and sorting the energy supply stations in the energy supply station recommendation list according to the personal preference scores to obtain a second recommendation list.
According to an embodiment of the present application, the personal preference score determining module 303 includes: the selection reasonability acquisition submodule is used for acquiring the selection reasonability of each energy supply station in the first recommendation list according to the historical behavior data, wherein the selection reasonability represents the difference degree between the predicted selection result and the actual selection result, and the selection reasonability is lower when the difference degree is larger; and the personal preference score determining submodule is used for determining the personal preference score of each energy supply station in the first recommendation list according to the selection reasonableness, so that the lower the selection reasonableness is, the higher the personal preference score is.
According to an embodiment of the present application, the personal preference score determining module 303 includes: the waiting time threshold acquisition submodule is used for acquiring the historical waiting time threshold of the user according to the historical behavior data; the current waiting time submodule is used for acquiring the waiting time of each energy supply station in the first recommendation list in the current time period; and the personal preference score determining submodule is used for determining the personal preference score of each energy supply station in the first recommendation list according to the waiting time and the historical waiting time threshold.
According to an embodiment of the present application, the personal preference score determining module 303 includes: the suspected destination determining submodule is used for determining a suspected destination of the user according to a destination corresponding to the current time period in the historical behavior data; the distance obtaining submodule is used for obtaining the distance between each energy supply station in the first recommendation list and the suspected destination; and the personal preference score determining sub-module is used for determining the personal preference score of each energy supply station in the first recommendation list according to the distance from the suspected destination.
According to an embodiment of the application, the suspected destination determining submodule is specifically configured to determine the company address of the user as the suspected destination of the user if the current time interval is the working time interval; and if the current time interval is the off-duty time interval, determining the home address of the user as the suspected destination of the user.
According to an embodiment of the present application, the personal preference score determining module 303 includes: the access frequency acquisition submodule is used for acquiring the access frequency of the user to each energy supply station in the first recommendation list according to the historical behavior data; and the personal preference scoring submodule is used for determining the personal preference score of each energy supply station in the first recommendation list according to the access frequency.
According to an embodiment of the application, determining the personal preference score of each energy supply station in the first recommendation list according to the historical behavior data comprises: acquiring selection factor preference of a user according to historical behavior data; a personal preference score for each energy delivery station in the first recommendation list is determined based on the selection factor preferences.
According to an embodiment of the application, determining the personal preference score of each energy supply station in the first recommendation list according to the historical behavior data comprises: obtaining an evaluation record of each energy supply station in a first recommendation list according to the evaluation record of the energy supply station in the historical behavior data; and determining the personal preference score of each energy supply station in the first recommendation list according to the evaluation records.
According to an embodiment of the application, determining the personal preference score of each energy supply station in the first recommendation list according to the historical behavior data comprises: according to at least two kinds of data in the historical behavior data, scoring each energy supply station in the first recommendation list respectively to obtain at least two scoring results; and weighting and summing at least two scoring results to obtain the personal preference score of each energy supply station.
According to an embodiment of the application, determining the personal preference score of each energy supply station in the first recommendation list according to the historical behavior data comprises: and determining the personal preference score of each energy supply station in the first recommendation list according to the historical behavior data and the user behavior machine learning model.
According to an embodiment of the application, after obtaining the second recommendation list, the method further includes: acquiring personal information and vehicle information of a user; and adjusting the personal preference scores of the second recommendation list according to the personal information and the vehicle information to obtain the adjusted personal preference scores.
According to an embodiment of the present application, acquiring personal information of a user includes: acquiring personal portrait information of a user; mapping the user to a corresponding user group according to the personal portrait information to obtain a first user group; correspondingly, the adjusting the personal preference score of the second recommendation list according to the personal information and the vehicle information to obtain the adjusted personal preference score comprises the following steps: and adjusting the personal preference scores of the second recommendation list according to the first user group preference and the vehicle information to obtain the adjusted personal preference scores.
According to a third aspect of embodiments herein, there is provided a computer storage medium comprising a set of computer executable instructions for performing any of the above methods when executed.
Here, it should be noted that: the above description on the embodiment of the information recommendation device and the above description on the embodiment of the computer storage medium are similar to the description on the embodiment of the foregoing method, and have similar beneficial effects to the embodiment of the foregoing method, and therefore, the description thereof is omitted. For technical details that have not been disclosed in the description of the embodiment of the information recommendation device and the embodiment of the computer storage medium, please refer to the description of the foregoing method embodiments of the present application for understanding, and therefore will not be described again for brevity.
It should be noted that, in this document, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.
In the several embodiments provided in the present application, it should be understood that the disclosed apparatus and method may be implemented in other ways. The above-described device embodiments are merely illustrative, for example, the division of a unit is only one logical function division, and there may be other division ways in actual implementation, such as: multiple units or components may be combined, or may be integrated into another device, or some features may be omitted, or not implemented. In addition, the coupling, direct coupling or communication connection between the components shown or discussed may be through some interfaces, and the indirect coupling or communication connection between the devices or units may be electrical, mechanical or other forms.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units; can be located in one place or distributed on a plurality of network units; some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, all functional units in the embodiments of the present application may be integrated into one processing unit, or each unit may be separately regarded as one unit, or two or more units may be integrated into one unit; the integrated unit can be realized in a form of hardware, or in a form of hardware plus a software functional unit.
Those of ordinary skill in the art will understand that: all or part of the steps for realizing the method embodiments can be completed by hardware related to program instructions, the program can be stored in a computer readable storage medium, and the program executes the steps comprising the method embodiments when executed; and the aforementioned storage medium includes: various media capable of storing program codes, such as a removable storage medium, a Read Only Memory (ROM), a magnetic disk, and an optical disk.
Alternatively, the integrated unit may be stored in a computer-readable storage medium if it is implemented in the form of a software functional module and sold or used as a separate product. Based on such understanding, the technical solutions of the embodiments of the present application may be essentially implemented or portions thereof that contribute to the prior art may be embodied in the form of a software product stored in a storage medium, and including several instructions for enabling a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the methods of the embodiments of the present application. And the aforementioned storage medium includes: a removable storage medium, a ROM, a magnetic disk, an optical disk, or the like, which can store the program code.
The above description is only for the specific embodiments of the present application, but the scope of the present application is not limited thereto, and any person skilled in the art can easily conceive of the changes or substitutions within the technical scope of the present application, and shall be covered by the scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.
Claims (14)
1. An information recommendation method, characterized in that the method comprises:
determining a first recommendation list of the energy supply station according to the positioning of the user;
acquiring historical behavior data associated with a user and an energy supply station;
determining a personal preference score for each energy supply station in the first recommendation list based on the historical behavior data;
and screening and sorting the energy supply stations in the first recommendation list according to the personal preference scores to obtain a second recommendation list.
2. The method of claim 1, wherein determining a personal preference score for each energy delivery station in the first recommendation list based on the historical behavior data comprises:
acquiring the selection reasonableness of each energy supply station in the first recommendation list according to the historical behavior data, wherein the selection reasonableness represents the difference degree between a predicted selection result and an actual selection result, and the larger the difference degree is, the lower the selection reasonableness is;
and determining the personal preference score of each energy supply station in the first recommendation list according to the selection reasonableness, so that the lower the selection reasonableness, the higher the personal preference score.
3. The method of claim 1, wherein determining a personal preference score for each energy delivery station in the first recommendation list based on the historical behavior data comprises:
acquiring a historical waiting time threshold of the user according to the historical behavior data;
acquiring the waiting time of each energy supply station in the first recommendation list in the current time period;
and determining the personal preference score of each energy supply station in the first recommendation list according to the waiting time and the historical waiting time threshold.
4. The method of claim 1, wherein determining a personal preference score for each energy delivery station in the first recommendation list based on the historical behavior data comprises:
determining a suspected destination of the user according to a destination corresponding to the current time period in the historical behavior data;
acquiring the distance between each energy supply station in the first recommendation list and a suspected destination;
and determining the personal preference score of each energy supply station in the first recommendation list according to the distance from the suspected destination.
5. The method of claim 4, wherein determining the suspected destination of the user from the destination in the historical behavior data corresponding to the current time period comprises:
if the current time interval is the working time interval, determining the company address of the user as the suspected destination of the user;
and if the current time interval is the off-duty time interval, determining the home address of the user as the suspected destination of the user.
6. The method of claim 1, wherein determining a personal preference score for each energy delivery station in the first recommendation list based on the historical behavior data comprises:
acquiring the access frequency of a user to each energy supply station in the first recommendation list according to the historical behavior data;
and determining the personal preference score of each energy supply station in the first recommendation list according to the access frequency.
7. The method of claim 1, wherein determining a personal preference score for each energy delivery station in the first recommendation list based on the historical behavior data comprises:
acquiring selection factor preference of a user according to the historical behavior data;
determining a personal preference score for each energy supply station in the first recommendation list based on the selection factor preferences.
8. The method of claim 1, wherein determining a personal preference score for each energy delivery station in the first recommendation list based on the historical behavior data comprises:
acquiring an evaluation record of each energy supply station in the first recommendation list according to the evaluation record of the energy supply station in the historical behavior data;
and determining the personal preference score of each energy supply station in the first recommendation list according to the evaluation record.
9. The method of claim 1, wherein determining a personal preference score for each energy delivery station in the first recommendation list based on the historical behavior data comprises:
according to at least two kinds of data in the historical behavior data, scoring each energy supply station in the first recommendation list respectively to obtain at least two scoring results;
and weighting and summing the at least two scoring results to obtain the personal preference score of each energy supply station.
10. The method of claim 1, wherein determining a personal preference score for each energy delivery station in the first recommendation list based on the historical behavior data comprises:
and determining the personal preference score of each energy supply station in the first recommendation list according to the historical behavior data and a user behavior machine learning model.
11. The method of claim 1, wherein after the obtaining the second recommendation list, the method further comprises:
acquiring personal information and vehicle information of a user;
and adjusting the personal preference score of the second recommendation list according to the personal information and the vehicle information to obtain the adjusted personal preference score.
12. The method of claim 11, wherein obtaining personal information of a user comprises:
acquiring personal portrait information of a user;
mapping the user to a corresponding user group according to the personal portrait information to obtain a first user group;
correspondingly, the adjusting the personal preference score of the second recommendation list according to the personal information and the vehicle information to obtain an adjusted personal preference score comprises the following steps:
and adjusting the personal preference scores of the second recommendation list according to the first user group preference and the vehicle information to obtain adjusted personal preference scores.
13. The method of any one of claims 1 to 12, wherein the historical behavioral data comprises at least one of location records, energy supply station search records, energy supply station navigation records, and energy supply station rating records.
14. An information recommendation apparatus, characterized in that the apparatus comprises:
the first recommendation list determining module is used for acquiring historical behavior data of the user related to the energy supply station;
the historical behavior data acquisition module is used for acquiring historical behavior data associated with the energy supply station by the user;
the personal preference score determining module is used for determining the personal preference score of each energy supply station in the first recommendation list according to the historical behavior data;
and the personalized screening and sorting module is used for screening and sorting the energy supply stations in the energy supply station recommendation list according to the personal preference scores to obtain a second recommendation list.
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