CN112115364A - Recommendation method and system for cold start based on login operation and computer equipment - Google Patents
Recommendation method and system for cold start based on login operation and computer equipment Download PDFInfo
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Abstract
The application relates to a recommendation method, a system, computer equipment and a computer readable storage medium for cold start based on login operation, wherein the recommendation method comprises the following steps: a data acquisition step, which is used for acquiring the information of the known user and the login operation information of the new user; a login information relation tree establishing step, which is used for establishing a login information relation tree through data analysis of the known user information; the login information relation tree comprises known user information and a corresponding recommendation information type; and a new user login recommending step, which is used for comparing and searching in the operation branch of the login information relation tree based on the login operation information of the new user to obtain an information type matched with the login operation information of the new user and using the information type as the new user login recommending information type. The corresponding recommended information type is automatically matched based on the login operation information of the user, the user does not need to select an interest tag, accurate and personalized information recommendation is achieved, and meanwhile user experience is improved.
Description
Technical Field
The present application relates to the field of internet technologies, and in particular, to a method and a system for recommending a cold boot based on a login operation, a computer device, and a computer-readable storage medium.
Background
Since the internet era is entered, with the continuous expansion of the scale of electronic commerce and the exponential progression increase of the information quantity, users face the trouble of information overload all the time, and the recommendation system can provide personalized decision support and information service for the users according to the interest characteristics and behaviors of the users, wherein a large amount of user data needs to be accumulated. If a new user cannot be given a good impression, it is difficult to retain the new user, and the user growth will be delayed. The internet economy is established on the basis of scale users, and the general health of the users is increased so as not to be eliminated by the large environment.
In order to provide interesting information for new users, the industry mainly carries out cold start according to the contents of a heat list or the first login and tag selection operation at present. According to the popular list content, the content or the commodities are subjected to hot ranking according to the existing data, popular content is recommended to a new user, and when user data are gradually huge, personalized recommendation is performed; and selecting one or more labels according to the first login, when a new user is required to log in, collecting and sorting the interested range of the user according to the selection, and recommending contents and commodities with high correlation.
However, the content of the popular list content is often related to real-time information, numerous complex channels are combined, the popular list content represents a concentrated focus of attention of the public at that time, the unity is achieved, the accuracy and the individuation of a single user cannot be achieved, and the user feels tired easily. According to the first login and selection of the tags, the tags provided by the authorities are usually fixed, and in order to hit more interests, the tags are relatively general, the recommended range after selection is wide, and accurate recommendation is difficult to achieve. If the number of the labels is large, the selection is complicated, so that new users have a bored mind and can be abandoned quickly.
More and more products put the recommended service at the most core location, or at the core of the entire product, and therefore new users have to face the problem of cold starts. For a new user, the user can be increased by recommending appropriate information to satisfy the new user. Due to complexity and uncertainty, accuracy and personalization of a single user cannot be achieved, and the user is prone to fatigue.
In summary, no effective solution has been proposed for the current recommendation of information for the precise personality of the user in cold start.
Disclosure of Invention
The embodiment of the application provides a recommendation method, a recommendation system, computer equipment and a computer readable storage medium for cold start based on login operation, corresponding recommendation information types are automatically matched based on login operation information of a user, the user does not need to select an interest tag, accurate and personalized information recommendation is achieved, and user experience is improved.
In a first aspect, an embodiment of the present application provides a recommendation method for performing cold boot based on a login operation, which at least includes:
a data acquisition step, which is used for acquiring the information of the known user and the login operation information of the new user;
a login information relation tree establishing step, which is used for establishing a login information relation tree through data analysis of the known user information; the login information relation tree comprises known user information and a corresponding recommendation information type;
and a new user login recommending step, which is used for comparing and searching in the operation branch of the login information relation tree based on the login operation information of the new user to obtain an information type matched with the login operation information of the new user and using the information type as the new user login recommending information type.
Through the steps, in the cold start process, the login operation information of the new user is associated with the recommended information type preferred by the user through establishing the login information relation tree, accurate and personalized information recommendation of the user is realized through comparing the login information relation tree, the matching efficiency and accuracy are improved, meanwhile, the recommendation method is not easy to generate the user fatigue, and the user experience is improved.
In some embodiments, the step of establishing the login information relationship tree further comprises:
a login operation Classification step, configured to classify according to login information in the known user information to obtain a login information relationship Tree, specifically, the Classification operation is based on a Classification algorithm model, such as a CART Classification decision Tree (CART for short);
a recommended information filling step, which is used for performing data mining according to the known user information to obtain a characteristic attribute matched with the login information of the known user information, inducing the characteristic attribute to obtain a recommended information type, and filling the recommended information type into the login information relation tree to obtain a recommended information type corresponding to each operation branch;
the characteristic attributes are used for specifically representing behavior tracks of interests and hobbies of the user, and based on the behavior tracks, the recommended information type preferred by the user is analyzed and positioned based on the login information of the known user on the premise that the information and the behaviors of the user are difficult to obtain, so that personalized recommendation is realized on the premise that the information of the user is incomplete.
In some embodiments, in the step of filling the recommendation information, when recommendation information types corresponding to a plurality of same login information are different, the matching degrees of the login information and the recommendation information types are calculated and ranked, so that a recommendation information type with a high matching degree is obtained, and the recommendation information type matching efficiency and matching accuracy of the method are improved.
In some embodiments, the login information of the known user information and the login operation information of the new user at least include: user registration information, user operation information and user login information; for example and without limitation, data analysis is performed on login date, login time, login mailbox, user name setting and registered mobile phone number of the user, such as: judging whether a user login date is a workday, judging whether a user login time period is early, medium or late, judging whether a postfix of a user login mailbox is a mainstream mailbox, an uncommon mailbox or a company mailbox, judging whether a user name of the user is a name, a number, a letter combination or other complex characters, judging the password difficulty of the user, judging whether a network to which the user registers a mobile phone number belongs and whether the user is a beautiful number, judging whether the user selects to enable a notification, judging whether the user selects to obtain a location, and the like.
In a second aspect, an embodiment of the present application provides a recommendation system for performing cold boot based on a login operation, which at least includes:
the data acquisition module is used for acquiring the information of the known user and the login operation information of the new user;
the login information relation tree establishing module is used for establishing a login information relation tree through data analysis of the known user information; the login information relation tree comprises known user information and a corresponding recommendation information type;
and the new user login recommendation module is used for comparing and searching in the operation branch of the login information relation tree based on the login operation information of the new user to obtain an information type matched with the login operation information of the new user and using the information type as the new user login recommendation information type.
Based on the modules, in the cold start process, the login operation information of the new user is associated with the preferred recommendation information type of the user by establishing the login information relation tree, accurate and personalized information recommendation of the user is realized by comparing the login information relation tree, the matching efficiency and accuracy are improved, and meanwhile, the recommendation system is not easy to generate the fatigue of the user in use and is beneficial to improving the user experience.
In some embodiments, the login information relationship tree establishing module further comprises:
the login operation classification module is used for classifying according to login information in the known user information to obtain a login information relation tree;
the recommendation information filling module is used for performing data mining according to the known user information to obtain a characteristic attribute matched with the login information of the known user information, inducing the characteristic attribute to obtain a recommendation information type, and filling the recommendation information type into the login information relation tree to obtain a recommendation information type corresponding to each operation branch;
the characteristic attributes are used for specifically representing behavior tracks of interests and hobbies of the user, and based on the behavior tracks, the recommended information type preferred by the user is analyzed and positioned based on the login information of the known user on the premise that the information and the behaviors of the user are difficult to obtain, so that personalized recommendation is realized on the premise that the information of the user is incomplete.
In some embodiments, in the recommendation information filling module, when recommendation information types corresponding to a plurality of same login information are different, the matching degrees of the login information and the recommendation information types are calculated and ranked, so that a recommendation information type with a high matching degree is obtained, and the recommendation information type matching efficiency and matching accuracy of the system are improved.
In some embodiments, the login information of the known user information and the login operation information of the new user at least include: user registration information, user operation information and user login information.
In a third aspect, an embodiment of the present application provides a computer device, which includes a memory, a processor, and a computer program stored on the memory and executable on the processor, where the processor, when executing the computer program, implements the recommendation method for performing cold boot based on login operation as described in the first aspect.
In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, on which a computer program is stored, where the computer program, when executed by a processor, implements the recommendation method for performing cold boot based on login operation as described in the first aspect above.
Compared with the related art, the recommendation method, the system, the computer device and the computer readable storage medium for cold start based on login operation provided by the embodiment of the application have the advantages that through the steps and the structure, in the cold start process, the login operation information of a new user is associated with the recommended information type preferred by the user through establishing the login information relation tree, the accurate and personalized information recommendation of the user is realized through comparing the login information relation tree, the matching efficiency and the accuracy are improved, meanwhile, the recommendation method is not easy to generate the feeling of fatigue of the user in use, and the user experience is facilitated to be improved; according to the embodiment of the application, on the premise that the user information and behaviors are difficult to obtain, the preferred recommendation information type of the user is analyzed and positioned on the basis of the login information of the known user, and therefore personalized recommendation is achieved on the premise that the user information is incomplete.
The details of one or more embodiments of the application are set forth in the accompanying drawings and the description below to provide a more thorough understanding of the application.
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 flowchart of a method for recommending a cold boot based on a login operation according to an embodiment of the present application;
FIG. 2 is a schematic diagram illustrating a principle of a recommended method for performing cold boot based on a login operation according to an embodiment of the application;
fig. 3 is a block diagram of a recommendation system for performing cold boot based on a login operation according to an embodiment of the present application.
Description of the drawings:
1. a recommendation system; 11. a data acquisition module; 12. a login information relation tree building module;
13. a new user logs in a recommendation module;
121. a login operation classification module; 122. and a recommendation information filling module.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the present application will be described and illustrated below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the present application and are not intended to limit the present application. All other embodiments obtained by a person of ordinary skill in the art based on the embodiments provided in the present application without any inventive step are within the scope of protection of the present application.
It is obvious that the drawings in the following description are only examples or embodiments of the present application, and that it is also possible for a person skilled in the art to apply the present application to other similar contexts on the basis of these drawings without inventive effort. Moreover, it should be appreciated that in the development of any such actual implementation, as in any engineering or design project, numerous implementation-specific decisions must be made to achieve the developers' specific goals, such as compliance with system-related and business-related constraints, which may vary from one implementation to another.
Reference in the specification to "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment can be included in at least one embodiment of the specification. The appearances of the phrase in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments. Those of ordinary skill in the art will explicitly and implicitly appreciate that the embodiments described herein may be combined with other embodiments without conflict.
Unless defined otherwise, technical or scientific terms referred to herein shall have the ordinary meaning as understood by those of ordinary skill in the art to which this application belongs. Reference to "a," "an," "the," and similar words throughout this application are not to be construed as limiting in number, and may refer to the singular or the plural. The present application is directed to the use of the terms "including," "comprising," "having," and any variations thereof, which are intended to cover non-exclusive inclusions; for example, a process, method, system, article, or apparatus that comprises a list of steps or modules (elements) is not limited to the listed steps or elements, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus. Reference to "connected," "coupled," and the like in this application is not intended to be limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. The term "plurality" as referred to herein means two or more. "and/or" describes an association relationship of associated objects, meaning that three relationships may exist, for example, "A and/or B" may mean: a exists alone, A and B exist simultaneously, and B exists alone. The character "/" generally indicates that the former and latter associated objects are in an "or" relationship. Reference herein to the terms "first," "second," "third," and the like, are merely to distinguish similar objects and do not denote a particular ordering for the objects.
The embodiment provides a cold start recommending method based on login operation. Fig. 1 is a flowchart of a recommendation method for performing cold boot based on a login operation according to an embodiment of the present application, and with reference to fig. 1, the flowchart includes the following steps:
a data obtaining step S1, configured to obtain the known user information and the login operation information of the new user.
A login information relationship tree establishing step S2 of establishing a login information relationship tree through data analysis of the known user information; the login information relation tree comprises known user information and corresponding recommendation information types.
And a new user login recommending step S3, configured to compare and search in an operation branch of the login information relationship tree based on the login operation information of the new user, obtain an information type matching the login operation information of the new user, and use the information type as a new user login recommendation information type.
Through the steps S1-S3, in the cold start process, the login operation information of the new user is associated with the recommended information type preferred by the user through establishing the login information relation tree, accurate and personalized information recommendation of the user is achieved through comparing the login information relation tree, the matching efficiency and accuracy are improved, meanwhile, the recommendation method is not prone to generating user fatigue, and the user experience is improved.
In some embodiments, the step of building a relationship tree of login information further comprises:
a login operation Classification step S201, configured to classify according to login information in known user information to obtain a login information relationship Tree, specifically, the Classification operation is based on a Classification algorithm model, such as a CART Classification decision Tree (CART);
a recommended information filling step S202, configured to perform data mining according to known user information to obtain a feature attribute matched with login information of the user, and summarize the feature attribute to obtain a recommended information type, and fill the recommended information type into a login information relationship tree to obtain a recommended information type corresponding to each operation branch, where it is noted that, when a plurality of recommended information types corresponding to the same login information are different, the matching degrees of the login information and the recommended information types are calculated and ranked to obtain a recommended information type with a high matching degree; the login information in the known user information and the login operation information of the new user at least comprise: user registration information, user operation information and user login information; for example and without limitation, data analysis is performed on login date, login time, login mailbox, user name setting and registered mobile phone number of the user, such as: judging whether a user login date is a workday, judging whether a user login time period is early, medium or late, judging whether a postfix of a user login mailbox is a mainstream mailbox, an uncommon mailbox or a company mailbox, judging whether a user name of the user is a name, a number, a letter combination or other complex characters, judging the password difficulty of the user, judging whether a network to which the user registers a mobile phone number belongs and whether the user is a beautiful number, judging whether the user selects to enable a notification, judging whether the user selects to obtain a location, and the like. The characteristic attributes are used for specifically representing behavior tracks of interests and hobbies of the user, and based on the behavior tracks, the recommended information types preferred by the user are analyzed and positioned based on the login information of the known user on the premise that the user information and behaviors are difficult to obtain, so that personalized recommendation is realized on the premise that the user information is incomplete.
The embodiment also provides a recommendation system for cold start based on login operation. Fig. 3 is a block diagram of a recommendation system for performing cold boot based on a login operation according to an embodiment of the present application. As shown in fig. 3, the recommendation system includes: the system comprises a data acquisition module, a login information relation tree establishing module, a new user login recommending module and the like. As used hereinafter, the terms "module," "unit," "subunit," and the like may implement a combination of software and/or hardware for a predetermined function. Although the means described in the embodiments below are preferably implemented in software, an implementation in hardware, or a combination of software and hardware is also possible and contemplated.
The following describes the components of the recommendation system 1 in detail with reference to fig. 3:
the data acquisition module 11 is used for acquiring the information of the known user and the login operation information of the new user;
a login information relation tree establishing module 12, configured to establish a login information relation tree through data analysis of known user information; the login information relation tree comprises known user information and a corresponding recommendation information type;
and the new user login recommending module 13 is configured to compare and search in the operation branch of the login information relationship tree based on the login operation information of the new user, obtain an information type matched with the login operation information of the new user, and use the information type as the new user login recommending information type.
Wherein, the login information relationship tree establishing module 12 further includes:
the login operation classification module 121 is configured to classify the login information according to login information in the known user information to obtain a login information relationship tree;
the recommended information filling module 122 is configured to perform data mining according to known user information to obtain a feature attribute matched with the login information of the user, generalize the feature attribute to obtain a recommended information type, and fill the recommended information type into the login information relationship tree to obtain a recommended information type corresponding to each operation branch; it should be noted that, in order to improve the matching efficiency and matching accuracy of the recommended information types of the system, when the recommended information types corresponding to a plurality of same login information are different, the matching degree between the login information and the recommended information types is calculated and ranked, so as to obtain the recommended information type with high matching degree.
Based on the modules, in the cold start process, the login operation information of the new user is associated with the preferred recommendation information type of the user by establishing the login information relation tree, accurate and personalized information recommendation of the user is realized by comparing the login information relation tree, the matching efficiency and accuracy are improved, and meanwhile, the recommendation system is not easy to generate the fatigue of the user in use and is beneficial to improving the user experience.
The login information in the known user information and the login operation information of the new user at least comprise: user registration information, user operation information and user login information; for example and without limitation, data analysis is performed on login date, login time, login mailbox, user name setting and registered mobile phone number of the user, such as: judging whether a user login date is a workday, judging whether a user login time period is early, medium or late, judging whether a postfix of a user login mailbox is a mainstream mailbox, an uncommon mailbox or a company mailbox, judging whether a user name of the user is a name, a number, a letter combination or other complex characters, judging the password difficulty of the user, judging whether a network to which the user registers a mobile phone number belongs and whether the user is a beautiful number, judging whether the user selects to enable a notification, judging whether the user selects to obtain a location, and the like. In addition, the characteristic attributes are used for specifically representing behavior tracks of interests and hobbies of the user, and based on the behavior tracks, the recommended information types preferred by the user are analyzed and positioned based on the login information of the known user on the premise that the user information and behaviors are difficult to obtain, so that personalized recommendation is realized on the premise that the user information is incomplete.
The embodiments of the present application are described and illustrated below by means of preferred embodiments.
Fig. 2 is a schematic diagram illustrating a principle of a recommended method for performing cold boot based on a login operation according to an embodiment of the application.
Referring to fig. 2, in the embodiment of the present application, after obtaining the known user information and the login operation information of the new user in step S1, in step S201, login operation classification is performed according to the known user information, and it is determined whether the login date is a workday, the login time is divided into morning, evening, login mailbox suffix mainstream mailbox, unusual mailbox, company mailbox, user name, number, letter combination, non-mainstream, password difficulty, network to which the registered mobile phone number belongs, whether it is a good number, whether it is to enable a notification, whether it is to select to obtain a location, and the like, thereby forming the login information relationship tree shown in fig. 2.
Then, filling the recommended information type for the login operation relationship tree according to the known user information through step S202; and when the recommendation information types corresponding to a plurality of same login information are different, sorting the recommendation information types according to the matching degree.
Finally, step S3 is executed, the login operation relationship tree is compared according to the login operation information of the new user, a corresponding login operation branch is searched, and the recommendation information type of the branch is obtained, so as to recommend the recommendation information of the type corresponding to the operation branch to the new user, such as: the internet, the tourism, the house and the home.
It should be noted that the steps illustrated in the above-described flow diagrams or in the flow diagrams of the figures may be performed in a computer system, such as a set of computer-executable instructions, and that, although a logical order is illustrated in the flow diagrams, in some cases, the steps illustrated or described may be performed in an order different than here.
In addition, the schematic diagram of the recommendation method for performing cold boot based on login operation according to the embodiment of the present application described in connection with fig. 1 may be implemented by a computer device, where the computer device includes a memory, a processor, and a computer program stored in the memory and executable on the processor, and when the computer program is executed by the processor, the recommendation method for performing cold boot based on login operation in the embodiment of the present application is executed.
In addition, in combination with the recommendation method for performing cold boot based on login operation in the foregoing embodiments, embodiments of the present application may provide a computer-readable storage medium to implement. The computer readable storage medium having stored thereon computer program instructions; the computer program instructions, when executed by a processor, implement any of the above-described embodiments of a method for recommending a cold boot based on a login operation.
The technical features of the embodiments described above may be arbitrarily combined, and for the sake of brevity, all possible combinations of the technical features in the embodiments described above are not described, but should be considered as being within the scope of the present specification as long as there is no contradiction between the combinations of the technical features.
The above-mentioned embodiments only express several embodiments of the present application, and the description thereof is more specific and detailed, but not construed as limiting the scope of the invention. It should be noted that, for a person skilled in the art, several variations and modifications can be made without departing from the concept of the present application, which falls within the scope of protection of the present application. Therefore, the protection scope of the present patent shall be subject to the appended claims.
Claims (10)
1. A recommendation method for cold start based on login operation is characterized by at least comprising the following steps:
a data acquisition step, which is used for acquiring the information of the known user and the login operation information of the new user;
a login information relation tree establishing step, which is used for establishing a login information relation tree through data analysis of the known user information; the login information relation tree comprises known user information and a corresponding recommendation information type;
and a new user login recommending step, which is used for comparing and searching in the operation branch of the login information relation tree based on the login operation information of the new user to obtain an information type matched with the login operation information of the new user and using the information type as the new user login recommending information type.
2. The method for recommending a cold boot based on a login operation according to claim 1, wherein said step of building a login information relationship tree further comprises:
a login operation classification step, which is used for classifying according to login information in the known user information to obtain a login information relation tree;
and a recommended information filling step, which is used for performing data mining according to the known user information to obtain a characteristic attribute matched with the login information of the known user information, inducing the characteristic attribute to obtain a recommended information type, and filling the recommended information type into the login information relation tree to obtain the recommended information type corresponding to each operation branch.
3. The recommendation method for cold start based on login operation according to claim 2, wherein in the recommendation information filling step, when the recommendation information types corresponding to a plurality of same login information are different, the matching degrees of the login information and the recommendation information types are calculated and sorted to obtain the recommendation information type with high matching degree.
4. The recommendation method for cold boot based on login operation of claim 1, wherein the login information of the known user information and the login operation information of the new user at least comprise: user registration information, user operation information and user login information.
5. A recommendation system for cold start based on login operation is characterized by at least comprising:
the data acquisition module is used for acquiring the information of the known user and the login operation information of the new user;
the login information relation tree establishing module is used for establishing a login information relation tree through data analysis of the known user information; the login information relation tree comprises known user information and a corresponding recommendation information type;
and the new user login recommendation module is used for comparing and searching in the operation branch of the login information relation tree based on the login operation information of the new user to obtain an information type matched with the login operation information of the new user and using the information type as the new user login recommendation information type.
6. The system of claim 5, wherein the login information relationship tree building module further comprises:
the login operation classification module is used for classifying according to login information in the known user information to obtain a login information relation tree;
and the recommendation information filling module is used for performing data mining according to the known user information to obtain a characteristic attribute matched with the login information of the known user information, inducing the characteristic attribute to obtain a recommendation information type, and filling the recommendation information type into the login information relation tree to obtain the recommendation information type corresponding to each operation branch.
7. The system of claim 6, wherein in the recommendation information filling module, when recommendation information types corresponding to a plurality of same login information are different, the matching degrees between the login information and the recommendation information types are calculated and sorted to obtain a recommendation information type with a high matching degree.
8. The system of claim 5, wherein the login information of the known user information and the login operation information of the new user at least comprise: user registration information, user operation information and user login information.
9. Computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the processor implements the recommended method for cold boot based on a login operation according to any one of claims 1 to 4 when executing the computer program.
10. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, implements the recommended method for cold boot based on login operation according to any one of claims 1 to 4.
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN113377999A (en) * | 2021-06-08 | 2021-09-10 | 张仲元 | Video pushing method, computer device and readable storage medium |
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113377999A (en) * | 2021-06-08 | 2021-09-10 | 张仲元 | Video pushing method, computer device and readable storage medium |
| CN113377999B (en) * | 2021-06-08 | 2024-02-20 | 张仲元 | Video pushing method, computer device and readable storage medium |
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