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WO2018157818A1 - Procédé et appareil d'inférence de préférence d'utilisateur, dispositif terminal et support d'informations - Google Patents

Procédé et appareil d'inférence de préférence d'utilisateur, dispositif terminal et support d'informations Download PDF

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Publication number
WO2018157818A1
WO2018157818A1 PCT/CN2018/077568 CN2018077568W WO2018157818A1 WO 2018157818 A1 WO2018157818 A1 WO 2018157818A1 CN 2018077568 W CN2018077568 W CN 2018077568W WO 2018157818 A1 WO2018157818 A1 WO 2018157818A1
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Prior art keywords
application
preference
weight
user
information
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English (en)
Chinese (zh)
Inventor
谢舒翼
刘森森
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Guangzhou UCWeb Computer Technology Co Ltd
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Guangzhou UCWeb Computer Technology Co Ltd
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    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q30/00—Commerce
    • G06Q30/02—Marketing; Price estimation or determination; Fundraising
    • G06Q30/0201—Market modelling; Market analysis; Collecting market data
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L9/00—Cryptographic mechanisms or cryptographic arrangements for secret or secure communications; Network security protocols
    • H04L9/40—Network security protocols
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q30/00—Commerce
    • G06Q30/02—Marketing; Price estimation or determination; Fundraising

Definitions

  • the present invention relates to the field of data mining technologies, and in particular, to a method, device, terminal device and storage medium for estimating end user preferences, and a method, device and terminal device for recommending information to a user using the estimated preferences.
  • terminals As a carrier of mobile services, terminals are an important entry point for market development, market maintenance, and data and information business development. How to effectively analyze the preferences of end users to recommend appropriate services according to the preferences of end users has become an urgent problem in the mobile field.
  • the user is concerned about the real headline.
  • the client's reading habits and preferences for the new user are completely unclear. In this case, how to accurately know the preferences of new users to improve user retention is particularly important.
  • a main object of the present invention is to provide a method, an apparatus, and a terminal device for estimating end user preferences, which are capable of predicting a preference of an end user according to application information acquired on the terminal device. Based on the predicted preferences, users can be accurately recommended for information.
  • a method for estimating end user preferences including: acquiring respective related information of a plurality of installed applications in a terminal, the related information including application attribute information and application usage information; and estimating the terminal according to the related information.
  • User preferences can be realized by analyzing the installed application on the terminal.
  • estimating the preference of the end user according to the related information may include: weighting the plurality of installed applications according to the application attribute information and/or the application usage information; extracting one or more from the application attribute information of the plurality of installed applications Keyword; generating a preference tag representing one end user preference from one or more keywords based on the application weight.
  • the user's preference can be inferred based only on the keyword information of the installed application on the terminal.
  • the application attribute information includes application installation information
  • weighting the plurality of installed applications according to the application attribute information and/or the application usage information may include: indicating that the application usage information is running and/or recently used for the application usage information. High weighting factor; assigns a low weighting factor to the application pre-installed by the application installation information; and assigns an intermediate weighting factor to other applications. This will improve the accuracy of preference speculation by increasing the weight of frequently used applications.
  • the weighting the plurality of installed applications according to the application attribute information and/or the application usage information may further include: indicating that the widely installed application is downgraded by the application installation information; and indicating that the application installation information is small
  • the installed application raises the weighting factor. This can further improve the accuracy of the preference speculation by reducing the weight of the less discriminating application.
  • the application weight can be calculated according to the following formula: among them, Is the application usage time weight, and the application usage time weighting formula is Where T is the usage time of the most recently used application, T average is the average usage time of all applications, ⁇ is constant; weight is the weighting factor assigned, the weight of the running and/or recently used application is 3, the system is pre-installed The weight of the application is 1, and the weight of other applications is 2; installNum is the installed amount of the application on the market.
  • the application attribute information may include application description information including an application name, an application classification, and/or an application description content having respective source weights, and extracting one or more from application attribute information of the plurality of installed applications.
  • the keywords may include: extracting one or more keywords from application description information of the plurality of installed applications; and determining weights of one or more keywords based on source weights of the keywords, wherein the weights are based on one or more based on the application weights
  • Generating a preference tag representing the end user preference among the plurality of keywords may include generating a preference tag representing the end user preference from the one or more keywords based on the application weight and the keyword weight.
  • the judgment dimension of the preference speculation scheme of the present invention is further increased by considering the keyword weights.
  • the keyword weights can be calculated based on the following formula:
  • the source weight of the application name and the application classification information is 1 and the source weight of the application description content is 0.3.
  • the tf of each keyword indicates the number of occurrences of the word in a single application, and the idf of each keyword indicates the application of statistics. The total number divided by the number of applications that have the word.
  • estimating the preference of the end user according to the related information may include: weighting the plurality of installed applications according to the application attribute information and/or the application usage information; selecting the installed application and the weight allocation thereof are similar to the terminal user.
  • Other users extract one or more end user application keywords from application attribute information of multiple installed applications of the end user; extract one or more other from application attribute information of multiple installed applications of other users
  • the user applies a keyword; generates a preference tag representing the end user preference from one or more end user keywords and one or more other user application keywords.
  • the target user's preference can be more accurately estimated.
  • one or more end user keywords are end user keyword vectors sorted by weight
  • one or more other user keywords are other user keyword vectors sorted by weight
  • Generating a preference tag representing the end user preference in the keyword and one or more other user application keywords may include mapping the end user keyword vector and other user keyword vectors to the classification tag vector to each obtain a weighted terminal user classification label vector N a and their weights ordered other users classification tag vector R u; respectively categorizing tags within R u N a and the weight normalized; combined normalized to N a and R u to give R a preference label vector; preference and the preference of the right label tags within tag preference vector R a weight normalized to obtain the normalized R a as a representative of the end-user preferences.
  • a R u may each multiplied by a factor of importance to the N and a right classification tags within R u with Thereby, the speculation deviation caused by the insufficient data is avoided.
  • the preference tag weights greater than the average weight in R a may be subjected to weight reduction iteration until the maximum tag weight is less than a predetermined threshold to obtain a preference tag representing the end user preference.
  • individual tag weights are prevented from being too large, ensuring a comprehensive and balanced acquisition of user preferences.
  • a method for recommending information to an end user comprising: obtaining end user preferences inferred according to the method described above; and recommending information to the user according to end user preferences.
  • the recommendation information to the user based on the end user preference includes recommendation information based on the end user's preference tag and/or tag weight; the recommendation information is news, articles, and/or advertisements.
  • an apparatus for estimating end user preferences including: an information acquiring unit, configured to acquire related information of each installed application in the terminal, where the related information includes application attribute information and application usage. Information; and a preference speculating unit for estimating end user preferences based on the related information.
  • the preference speculating unit may include: an application weight allocating unit, configured to perform weight allocation on the plurality of installed applications according to the application attribute information and/or the application usage information; and a keyword extracting unit, configured to use the plurality of installed applications Extracting one or more keywords from the application attribute information; and a preference tag generating unit configured to generate a preference tag representing the end user preference from the one or more keywords based on the application weight.
  • an application weight allocating unit configured to perform weight allocation on the plurality of installed applications according to the application attribute information and/or the application usage information
  • a keyword extracting unit configured to use the plurality of installed applications Extracting one or more keywords from the application attribute information
  • a preference tag generating unit configured to generate a preference tag representing the end user preference from the one or more keywords based on the application weight.
  • the preference estimation unit may further include: another user selection unit, configured to select the installed user and other users whose weight assignment is similar to the terminal user, and other user keyword extraction units, for using other One or more other user application keywords are extracted from the application attribute information of the plurality of installed applications of the user, wherein the preference tag generating unit further generates a preference tag based on one or more other user application keywords.
  • another user selection unit configured to select the installed user and other users whose weight assignment is similar to the terminal user
  • other user keyword extraction units for using other One or more other user application keywords are extracted from the application attribute information of the plurality of installed applications of the user
  • the preference tag generating unit further generates a preference tag based on one or more other user application keywords.
  • the one or more end user keywords are end user keyword vectors sorted by weight
  • one or more other user keywords are other user keyword vectors sorted by weight
  • the preference speculating unit may further include: classification label vector mapping means for mapping the end-user keyword vector and other vectors to the user keyword classification tag vectors to obtain a weight by each end-user classification tag reordering and N a vector sorted by other users weight vector classification label U R; normalization unit, respectively, for N, and a right classification label R U in the normalized weight; combining unit for combining the normalized and the N R U to give a preference label vector R a, wherein the normalization unit further preference label on the right in preference label R a weight vector is normalized to obtain the normalized as a representative of R a preference label end user preferences.
  • the preference estimation unit may further comprise: right down iteration unit for, after obtained by normalizing R a, for R a greater than average weight preference label weights down the right iterated until the maximum label weight less than The threshold is predetermined to get a preference tag that represents the end user's preferences.
  • an information recommendation apparatus for an end user, comprising: the estimation apparatus described above, the estimation apparatus estimates an end user preference; and the information recommendation apparatus is configured to be estimated based on the estimation apparatus. End user preferences recommend information to the user.
  • a terminal device includes: a memory for storing an installed application and related information of the application, the related information including application attribute information and application usage information; and a processor connected to the memory And for: obtaining relevant information of each of the installed applications in the terminal; and estimating the end user preference according to the related information.
  • an electronic device readable storage medium comprising a program, when the program is run on an electronic device, causing the electronic device to perform the speculative method of the end user preference described in any of the foregoing .
  • an electronic device readable storage medium comprising a program, when the program is run on an electronic device, causing the electronic device to perform the information recommendation for the end user described in any of the foregoing method.
  • the method, device, recommendation method/device, terminal device and electronic device readable storage medium for performing the estimation method/recommendation method of the terminal user preference of the present invention starting from an application installed on the terminal, by using a plurality of installed applications Analysis of the respective relevant information can be used to infer the preferences of the end user.
  • FIG. 1 is a functional block diagram showing a terminal device according to an embodiment of the present invention.
  • FIG. 2 is a schematic flow chart showing a method of estimating end user preferences according to an embodiment of the present invention.
  • FIG. 3 is a schematic flow chart showing the estimation of a terminal user's preference based on related information, according to an embodiment of the present invention.
  • FIG. 4 is a schematic flowchart showing estimation of a terminal user's preference based on related information according to another embodiment of the present invention.
  • FIG. 5 is a schematic flowchart showing estimation of a terminal user's preference based on related information according to another embodiment of the present invention.
  • FIG. 6 is a functional block diagram showing a speculative device of an end user preference in accordance with an embodiment of the present invention.
  • FIG. 7 is a functional block diagram showing an information recommending apparatus for an end user according to an embodiment of the present invention.
  • FIG. 1 is a functional block diagram showing a terminal device according to an embodiment of the present invention.
  • the terminal device 100 shown in FIG. 1 may be a smart phone (for example, ), tablet (for example, ), portable computers and other mobile terminal devices.
  • the terminal device 100 may include at least a memory 110 and a processor 120 connected to the memory 110.
  • the memory 110 can store installed applications and related information of the applications. Related information may include application attribute information and application usage information.
  • the processor 120 may acquire relevant information of each of the plurality of installed applications in the terminal, and infer the end user preference according to the related information. For a specific processing procedure of the processor 120, reference may be made to FIG. 2.
  • FIG. 2 is a schematic flowchart showing a method for estimating end user preferences according to an embodiment of the present invention.
  • step S210 may be first performed to acquire related information of multiple installed applications in the terminal, where the related information includes application attribute information and application usage information.
  • the terminal described herein may preferably be a plurality of mobile terminal devices such as a smart phone, a tablet computer or a portable computer.
  • Various operating systems such as iOS, Android, or Windows are installed on the terminal, and various applications can be installed in the operating system.
  • Applications installed on the terminal can include pre-installed applications and user-defined installed applications.
  • the application attribute information of the application refers to various types of information related to the application itself, and may include information such as application installation information (for example, whether the system is pre-installed), application description information (such as an application name, an application classification, and an application description content).
  • Application usage information refers to usage status information applied to the terminal, such as whether it is running, recently running, and running time.
  • step S220 may be performed to infer the terminal according to the related information. User preferences.
  • the speculative scheme of the present invention starts from an application installed on a terminal, and can analyze the preference of the end user by analyzing the related information of each of the installed applications.
  • the preferences mentioned in the present invention may include user characteristics, behavior preferences, and the like of the end user. For example, the gender of the end user, shopping preferences, information browsing preferences, and the like may be inferred based on the speculative scheme of the present invention, that is, the user may be utilized.
  • the inventive speculative solution establishes a user portrait of the end user, thereby facilitating the recommendation of appropriate business information to the user based on the user's portrait.
  • the specific implementation process of estimating the preference of the end user based on the related information in the speculative method of the present invention will be described below.
  • the present invention details various ways of inferring the preferences of end users.
  • the user's preference may be estimated only according to the related information of the installed application on the terminal of the terminal user to be inferred, or may be found on the terminal of the user to be guessed according to the installed application on the terminal of the terminal user to be estimated.
  • the application is similar to the other one or more end users, and then the preferences of the end users to be inferred are inferred based on the preferences of the other one or more end users.
  • the above two methods can also be combined to comprehensively speculate the preferences of the end users to be inferred. The above three estimation methods are described below.
  • FIG. 3 is a schematic flow chart showing the estimation of a terminal user's preference based on related information, according to an embodiment of the present invention.
  • step 310 weight allocation is performed on a plurality of installed applications according to application attribute information and/or application usage information.
  • the installed application may be weighted according to the application attribute information and the application usage information, or the installed application may be weighted according to the application attribute information and the application usage information.
  • the application attribute information includes application installation information that can indicate whether the application is pre-installed by the system.
  • the pre-installed application of the system cannot fully represent the user's preference, so it is possible to assign a lower weighting factor, such as 1, to the application pre-installed by the application installation information.
  • Non-system pre-installed applications are user-defined applications installed on the terminal, which can represent the user's preferences to a certain extent, so it is possible to assign a higher weighting factor to the application installation information indicating that the non-system pre-installed application, for example 2.
  • the application usage information includes usage status information applied to the terminal, such as whether it is running, recently running, and running time.
  • the running or recently running application can represent the user's preferences, so the application usage information can be used to indicate that the running and/or recently running application is assigned a higher weighting factor, such as 3. For applications that are not running and/or recently running, they can be assigned a lower weighting factor, such as 2.
  • the application usage information may be used to indicate that the running and/or recently used application is assigned a high weighting factor, for example, 3.
  • a low weighting factor such as 1 is assigned to the application pre-installed by the application installation information.
  • a higher weighting factor assigned to it may be selected. For example, for an application pre-installed on a system that is being used and/or recently used, the weighting factor assigned to it based on the application installation information is 1, and the weighting factor assigned to it based on the application usage information is 3.
  • a weighting factor of 3 is used as the weight of the application.
  • the present invention can also indicate that the application installation information indicates that the widely installed application has a lower weighting factor, and that the application installation information indicates that the application is increased by a small range.
  • T is the usage time of the most recently used application
  • T average is the average usage time of all applications
  • ⁇ is a constant.
  • the present invention can calculate the weight of the application according to the following formula:
  • T is the usage time of the most recently used application
  • T average is the average usage time of all applications
  • ⁇ is a constant.
  • Weight is the assigned weighting factor.
  • the weight of the running and/or recently used application is 3.
  • the weight of the application pre-installed by the system is 1, and the weight of other applications is 2.
  • the installNum is the installed amount of the application on the market.
  • one or more keywords are extracted from application attribute information of a plurality of installed applications.
  • the application attribute information includes application description information
  • the application description information includes information such as an application name, an application classification, and an application description content. It is therefore possible to extract one or more keywords from the application description information of each of the plurality of installed applications.
  • the application name, the application classification, and the application description content of the installed application may be separately segmented, and the obtained word segmentation result may be used as a keyword.
  • the keyword weights of each keyword can also be calculated, and the process of calculating the keyword weights is as follows.
  • the source weights may be set in advance for the application name, the application classification, and the application description content.
  • the source weight of the application name and the application classification may be set to 1
  • the source weight of the application description content may be set to 0.3.
  • the weight of the keyword can be determined based on the source of the keyword.
  • the keyword weights can be calculated according to the following formula:
  • weight represents the source weight
  • the source weight of the application name and the application classification information is 1 and the source weight of the application description content is 0.3
  • the tf of each keyword indicates the number of times the word is applied in a single application
  • the idf of each keyword It is the total number of applications counted by the statistics divided by the number of applications that have the word.
  • a preference tag representing an end user preference is generated from one or more keywords based on the application weight.
  • the keywords may be arranged in order of the application weight of the application to which the keyword belongs, to select a keyword with a larger application weight, and then generate a preference tag representing the preference of the terminal user based on the selected keyword.
  • the selected keyword can be directly used as a preference tag representing the preference of the end user, or the selected keyword can be mapped to one or more classification tags (for example, social, entertainment, technology, politics, sports, etc.)
  • the classification tag is used as a preference tag representing the end user preference. For example, you can use a synonym relationship to map a keyword to a small label under a large label and then classify it into a large label.
  • keywords when extracting keywords, it is also possible to calculate keyword weights of keywords. It is therefore also possible to generate a preference tag representing the end user preference from one or more keywords based on the keyword weight. For example, a keyword with a higher keyword weight may be selected from the extracted keywords, and a preference tag representing the preference of the terminal user may be generated based on the selected keyword.
  • the process of generating a preference tag based on a keyword can be referred to the above description, and details are not described herein again.
  • a preference tag representing the end user preference may be generated from the extracted keywords based on the application weight and the keyword weight. For example, a keyword corresponding to an application with a larger application weight may be selected from the extracted keywords, and then keywords with a larger keyword weight may be further selected from the selected keywords, and then further filtered based on the selected keywords. The keyword generates a preference tag that represents the end user preference.
  • FIG. 4 is a schematic flowchart showing estimation of a terminal user's preference based on related information according to another embodiment of the present invention.
  • step 410 weight allocation is performed on a plurality of installed applications according to application attribute information and/or application usage information.
  • step S410 weight allocation is performed on a plurality of installed applications according to application attribute information and/or application usage information.
  • step S420 the installed application and its weight distribution are selected to be a predetermined number of other users similar to the terminal user to be speculated (for convenience of distinction, it may be referred to herein as a reference user, the same below).
  • the installed applications on the terminal of the end user may be arranged in order of the size of the application weights to obtain the application list vector V a .
  • the installed applications on the terminals of the other one or more end users are arranged in order of the size of the application weights to obtain one or more application list vectors V b .
  • the degree of similarity may be calculated between the vectors V a and V b the vector by a variety of ways, for example, the similarity between the vector and the vector V a V b is calculated by the cosine similarity.
  • the similarity between the vector V a and the vector V b can also be calculated according to the following formula:
  • V a ⁇ V b represents the intersection of the user a and the user b application list vector.
  • the preference of the reference user can be regarded as the preference of the terminal user to be guessed (step S430). That is to say, the preference tag representing the preference of the terminal user to be guessed can be obtained by seeking a preference tag representing the preference of the reference user.
  • the other users mentioned in step S420 may preferably be the end users whose preference tags have been determined, such that the preference tag of the reference user determined to be the end user to be inferred may be directly regarded as the preference tag of the end user to be inferred.
  • the preference tag of the reference user may be determined by referring to the method shown in FIG. 2 above, and details are not described herein again.
  • the union of the preference labels of the plurality of reference users may be taken, and then the average weight of all the labels in the set is calculated, and the predetermined number of times are selected according to the order of the weights.
  • the tag acts as a preference tag for the end user to be guessed.
  • Embodiment 1 makes speculation based on the information of the installed application on the terminal of the user to be guessed. If the application installation is too small, sparse data will cause a certain speculation bias.
  • Embodiment 2 only speculates the preference of the user to be speculated based on the preference of the reference user, and may also deviate from the preference of the target user. Therefore, in Embodiment 3, the above embodiments can be combined. This embodiment will be described in detail as follows. For the content that has been mentioned above, reference may be made to the above related description, and details are not described herein again.
  • FIG. 5 is a schematic flowchart showing estimation of a terminal user's preference based on related information according to another embodiment of the present invention.
  • step S510 weight allocation is performed on a plurality of installed applications according to application attribute information and/or application usage information.
  • step S520 the installed application and its weight assignment are selected to be a predetermined number of other users similar to the end user.
  • step S530 one or more end user application keywords are extracted from application attribute information of a plurality of installed applications of the terminal user.
  • step S540 one or more other user application keywords are extracted from application attribute information of a plurality of installed applications of other users.
  • a preference tag representing the end user preference is generated from the end user keyword and other user application keywords.
  • the terminal user keyword and other user application keywords may be arranged in order of the application weight of the application to which the keyword belongs, to select a keyword with a larger application weight, and then generate a representative terminal user based on the selected keyword.
  • Preferred preference tag the selected keyword can be directly used as a preference tag representing the preference of the end user, or the selected keyword can be mapped to one or more classification tags (for example, social, entertainment, technology, politics, sports, etc.)
  • the classification tag is used as a preference tag representing the end user preference.
  • the end user keyword may be an end user keyword vector sorted by weight, and other user keywords may be other user keyword vectors sorted by weight.
  • End-user keyword vectors and other vectors user keyword classification tag may be mapped to a vector, to give each end user sorted by weight N a classification tag vector and sorted according to the weight vectors of other users classification label R u.
  • N weights a and classification tags within R u weight normalized combined normalized to N a and R u to give preference label vector R a may prefer
  • the weights of the preference tags within the tag vector R a are normalized to obtain a normalized R a as a preference tag representing the end user preferences.
  • N a label may be less, but the right one or a few labels preference weight may be large, so for convenience of calculation, before combining the normalized and N a R u may also respectively N
  • the preference tag weights greater than the average weight in R a may be iterated until the maximum tag weight is less than a predetermined threshold to obtain the representative terminal.
  • a predetermined threshold For example, it may be assumed greater than the mean weight of R a The smallest of the weights is w i , and all the weights of the labels greater than the average weight are divided by the weight reduction factor. This is iterated until the maximum tag weight is less than a certain set threshold (eg 25%).
  • the estimation method of the end user's preference of the present invention has been described in detail with reference to FIGS. 2 to 5.
  • the present invention also proposes an information recommendation method for the end user.
  • the estimation method of the terminal user can be inferred by using the above-mentioned estimation method, and then appropriate information is recommended to the terminal user according to the acquired preference of the terminal user.
  • the information may be recommended based on the end user's preference tag and/or tag weight, which may be information such as news, articles, or advertisements.
  • the speculative/recommended method of the present invention can be applied to various scenarios, for example, it can be applied to user behavior estimation, commodity estimation, and is particularly suitable for a news recommendation end such as today's headline.
  • the news recommendation client needs to make appropriate recommendations based on big data and centered on user interests.
  • personalized news recommendations tailored to the user need to be based on the user's existing data and feedback.
  • the news client's reading habits for this new user are completely unclear.
  • how to predict the user's news reading preferences has become a problem, and the user's initial reading of the news reading can effectively improve the user's retention rate if predicted.
  • the recommendation strategies of some mainstream news clients in the process of cold start are mainly to push some current hot news, fine articles, and widely spread the net to explore the user's reading interest in all directions. Then, according to the user's reading behavior, the recommendation algorithm is gradually revised, the user's news reading portrait is improved, and the accurate recommendation is further made. This process is slightly slow.
  • the speculative/recommended method proposed by the present invention can be used to obtain a list of applications installed on a terminal such as a mobile phone, a list of recently used applications, a list of running applications, and the like, based on the similarity Collaborative filtering is performed by the user's reading interest of the application list and the algorithm of the user application list mapping to the news tag vector, and then considering the application usage time, system application, averaging, avoiding the proportion of individual recommendation tags being too large, etc. Weighted processing. Finally, the recommendation vector of a user news interest tag and the recommended weight of each tag are obtained and based on this, the cold start user is recommended for news.
  • the following is a description of the cold start process of the news client installed on the smartphone by the speculative/recommended method of the present invention.
  • the process is mainly divided into the following steps.
  • T average is the average duration of all applications and ⁇ is a constant.
  • the weight is marked as 3
  • the system preloaded application weight weight is marked as 1
  • the remaining weights are weighted as 2, press
  • the value of the application list is sorted from large to small to get the application list vector V a .
  • the weight of the word segmentation weight of the application name and the application classification information is 1, and the weight of the application description segmentation weight is 0.3, and a keyword vector is obtained by sorting the weighted word weight*log(tf*idf) from high to low.
  • the keyword vector applied to the classification label vector map News N a can take advantage of keyword synonyms relationship first big hit The small label below the label, and then the keyword is classified as a large label).
  • Each tag in the classification tag vector N a corresponds to a weight. This weight is derived from the keyword vector weight in step 7. When a tag is hit by multiple keywords, the one with the highest weight is taken.
  • N a label in all of the weight is multiplied by a factor of importance
  • All label weights in R u are also multiplied by an importance factor
  • the final news recommendation vector tag needs to merge the vectors in N a and R u .
  • the recommended tag vector R a is obtained by sorting the weights from large to small.
  • the recommended news tag vector obtained after the iteration of step 16 is completed.
  • the news can be recommended by referring to the weight of each tag in the tag vector.
  • the present invention also proposes an estimation device, a recommendation device, and a terminal device.
  • FIG. 6 is a functional block diagram showing a speculative device of an end user preference in accordance with an embodiment of the present invention.
  • the functional modules of the speculative device 600 may be implemented by hardware, software, or a combination of hardware and software that implements the principles of the present invention.
  • FIG. 5 can be combined or divided into sub-modules to implement the principles of the above invention. Accordingly, the description herein may support any possible combination, or division, or further limitation of the functional modules described herein.
  • the speculative device 600 shown in FIG. 6 can be used to implement the speculative method shown in FIG. 2 to FIG. 5.
  • the speculative device 600 can have only the functional modules that the speculative device 600 can have and the operations that can be performed by the functional modules are briefly described. For details, please refer to the description above with reference to FIG. 2 to FIG. 5, and details are not described herein again.
  • the speculative device 600 includes an information acquiring unit 610 and a preference estimating unit 620.
  • the information obtaining unit 610 is configured to acquire related information of each of the installed applications in the terminal, and the related information includes application attribute information and application usage information.
  • the preference speculating unit 620 is configured to infer an end user preference based on the related information.
  • the preference speculating unit 620 may optionally include an application weight allocating unit 621, a keyword extracting unit 622, and a preference label generating unit 623.
  • the application weight allocating unit 621 is configured to perform weight allocation on the plurality of installed applications according to the application attribute information and/or the application usage information.
  • the keyword extracting unit 622 is configured to extract one or more keywords from the application attribute information of the plurality of installed applications.
  • the preference tag generating unit 623 is configured to generate a preference tag representing the end user preference from the one or more keywords based on the application weight.
  • the preference speculative unit 620 can also optionally include other user selection units 624 and other user keyword extraction units 625.
  • the other user selection unit 624 is configured to select the installed user and other users whose weight assignment is similar to the terminal user.
  • the other user keyword extraction unit 625 is configured to extract one or more other user application keywords from application attribute information of a plurality of installed applications of other users.
  • the preference tag generating unit 623 can also generate a preference tag based on one or more other user application keywords.
  • the end user keyword may be an end user keyword vector sorted by weight, and other user keywords may be other user keyword vectors sorted by weight, and the preference speculating unit 620 may optionally include a classification label vector.
  • Classification label vector mapping unit 626 for mapping the end-user keyword vector and other vectors to the user keyword classification tag vectors to obtain a weight by each end-user classification tag reordering and N a vector sorted by other users weight vector classification label R u .
  • Normalization unit 627 respectively, for the right classification tags within R u N a normalized and weight.
  • Merging unit 628 for merging normalized to N a and R u to give preference label vector R a, wherein the normalization unit 627 further to the right preference label in preference label vector R a weight normalized to give after The normalized Ra is used as a preference tag representing the end user preferences.
  • the preference estimation unit may optionally include a further iteration unit 629 down the right, after obtaining a normalized by R a, R a greater than the average of the weighted preference label weights down the right iteration, Until the maximum tag weight is less than a predetermined threshold to get a preference tag that represents the end user preference.
  • FIG. 7 is a functional block diagram showing an information recommending apparatus for an end user according to an embodiment of the present invention.
  • the functional modules of the recommendation device 700 may be implemented by hardware, software, or a combination of hardware and software that implements the principles of the present invention.
  • Those skilled in the art can understand that the functional modules described in FIG. 6 can be combined or divided into sub-modules to implement the principles of the above invention. Accordingly, the description herein may support any possible combination, or division, or further definition of the functional modules described herein.
  • the recommendation device 700 includes an estimation device 600 and an information recommendation device 710.
  • the speculative device 600 can be used to infer the end user preference.
  • the information recommendation means 710 is for recommending information to the user based on the end user preference estimated by the estimation means 500.
  • the terminal device shown in FIG. 1 can also be used to implement the recommendation device 700 shown in FIG. 7 and its recommended method.
  • the present invention also provides an electronic device readable storage medium comprising a program, when executed on an electronic device, causing the electronic device to perform the method of estimating the preference of the end user based on the related information as described in any of the above embodiments.
  • the present invention further provides an electronic device readable storage medium comprising a program, when executed on an electronic device, causing the electronic device to perform the information recommendation method for the end user described in any of the above embodiments.
  • the above program includes computer program code instructions for performing the various steps defined above in the above method of the present invention.
  • the method according to the invention may also be embodied as a computer program product comprising a computer readable medium on which is stored a computer for performing the above-described functions defined in the above method of the invention program.
  • the various illustrative logical blocks, modules, circuits, and algorithm steps described in connection with the disclosure herein may be implemented as electronic hardware, computer software, or combinations of both.
  • each block of the flowchart or block diagram can represent a module, a program segment, or a portion of code that includes one or more of the Executable instructions.
  • the functions noted in the blocks may also occur in a different order than the ones in the drawings. For example, two consecutive blocks may be executed substantially in parallel, and they may sometimes be executed in the reverse order, depending upon the functionality involved.
  • each block of the block diagrams and/or flowcharts, and combinations of blocks in the block diagrams and/or flowcharts can be implemented in a dedicated hardware-based system that performs the specified function or operation. Or it can be implemented by a combination of dedicated hardware and computer instructions.

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Abstract

L'invention concerne un procédé et un appareil permettant d'inférer une préférence d'un utilisateur, un dispositif terminal (100) et un support d'informations. Le procédé d'inférence consiste : à obtenir des informations associées de chaque application de multiples applications installées dans un terminal (S210), les informations associées comprenant des informations d'attribut d'application et des informations d'utilisation d'application ; et à inférer une préférence d'un utilisateur de terminal selon les informations associées (S220). Par conséquent, une préférence d'un utilisateur de terminal peut être inférée par l'analyse d'informations associées de multiples applications installées. En outre, davantage d'informations peuvent être envoyées plus précisément à l'utilisateur en fonction de la préférence inférée.
PCT/CN2018/077568 2017-03-02 2018-02-28 Procédé et appareil d'inférence de préférence d'utilisateur, dispositif terminal et support d'informations Ceased WO2018157818A1 (fr)

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