Personalized community recommendation method based on user behaviors

A recommendation method and community technology, applied in data processing applications, special data processing applications, instruments, etc., can solve problems such as low novelty and inaccurate recommendation results

Active Publication Date: 2018-02-09
TIANJIN UNIVERSITY OF TECHNOLOGY
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0005] The purpose of the present invention is to solve the problem of inaccurate recommendation results and low novelty of the traditional personalized recommendation algorithm, optimize and improve the existing Jaccard similarity calculation method and PageRank algorithm, and design a personalized personalized recommendation algorithm based on user behavior community recommendation method

Method used

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  • Personalized community recommendation method based on user behaviors
  • Personalized community recommendation method based on user behaviors
  • Personalized community recommendation method based on user behaviors

Examples

Experimental program
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example 1

[0072] We verify the correctness and effectiveness of the personalized community recommendation algorithm based on user behavior through experiments, and verify the performance of the algorithm by comparing with related algorithms. We first verify the effectiveness of the first part of the algorithm. The traditional Jaccard calculation method is used as a comparison method, abbreviated as TJac. The personalized community recommendation algorithm is denoted as PCR. The evaluation quality of the top k users to be recommended is evaluated by the following two indicators: (1) the average accuracy rate AP@k of the top k recommendations; (2) the average MAP@k of the average accuracy rate of the top k recommendations. Here Set k to k=5, 10, 15, 20, respectively.

[0073] Figure 5 is the influence of different values ​​of parameter α on the recommendation quality.

[0074] Table 1 shows the influence of different values ​​of α and the recommended number k on the recommendation qu...

example 2

[0082] In the second part of our experiments, we verify the correctness and effectiveness of the personalized community recommendation algorithm based on user behavior, and verify the performance of the algorithm by comparing it with related algorithms. The influence of Weibo users is analyzed through experiments, and the experimental results are as follows. The PageRank algorithm is used as a comparison method (PR). Table 3 is the top 20 users obtained by the PR algorithm. Table 4 is the top 20 users obtained by the PCR algorithm.

[0083] Table 3 PR algorithm top 20 users

[0084]

[0085] Table 4 PCR algorithm top 20 users

[0086]

[0087] We can see that the UBR value and PR value have little difference in relative size, but the network nodes obtained by ranking are not the same. In order to compare bloggers’ rankings in their respective algorithms more clearly and discover the superiority of the algorithms, such as Image 6 shown. from Image 6 It is easy to...

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Abstract

The invention discloses a personalized community recommendation method based on user behaviors, and relates to social networks. Social network micro-blogs are used as a platform to analyze multi-attribute information of static attributes and dynamic attributes of users. Firstly, two aspects of bloggers followed by the micro-blog users and communities which the micro-blog users participate in are considered in a process of calculating user similarity degrees, and a traditional Jaccard similarity degree calculation method is extended to obtain a user similarity set; and then the similarity set is further screened from a perspective of user influences. The influences of the micro-blog users in the communities are related to numbers of fans thereof, and also to numbers of comments and forwarding on the micro-blogs thereof. On the basis thereof, a traditional PageRank algorithm is improved to calculate the user influences; and finally, Top-N is utilized to sort influence sizes to obtain final recommendation object sets. Experiment proves that an algorithm of the invention effectively solves the problem of inaccuracy of results obtained by traditional personalized recommendation algorithms, and greatly improves a surprise degree and novelty of recommendation.

Description

technical field [0001] The invention relates to a social network and a recommendation system, and specifically provides a personalized community recommendation method based on user behavior. Background technique [0002] With the rapid development and rise of the Internet and social networks, the user-centered information production model has caused the explosive growth of these information. Facing the incoming of such a vast amount of information, how Internet users choose, filter, and filter their favorite information has become the focus of current research. On social platforms, users are not only consumers of information, but also transmitters and producers of information. A social network is a collection of relationships formed by a huge network of users through self-organization. Internet users are constantly communicating with users, and these behavior records have contributed to the arrival of the era of big data. Users are willing to share their thoughts on socia...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F17/30G06Q50/00
CPCG06Q50/01G06F16/9535
Inventor 李文杰于笑明薛花张德干
Owner TIANJIN UNIVERSITY OF TECHNOLOGY
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