Network individual recommendation method based on PageRank algorithm

Abstract

The invention discloses a socialization filtering method based on a PageRank algorithm, mainly solving the problem that a filtering method has low accuracy under the conditions that group members are numerous and social relationship is complex in the prior art. The socialization filtering method disclosed by the invention is realized by the following steps: acquiring friend relationship between a group and the group members from a webpage configuration file, creating a personal preference model of each group member; by adopting the PageBank algorithm, iteratively calculating the influence of the group members to the group, so as to obtain a preference model of the whole group; carrying out object recommendation by utilizing the model, namely initiatively providing recommended object data for a user; selecting interested and required information from the information by the user. The socialization filtering method disclosed by the invention has the advantages that the preference model of the group is analyzed, and recommendation of different field objects on a network can be realized only by modifying a keyword vector in the field of the model.

Description

technical field [0001] The invention belongs to the technical field of information processing and relates to collaborative filtering, in particular to a network personalized recommendation system based on PageRank algorithm, which can be used for information mining and information service in the network. Background technique [0002] The rapid growth of the scale and coverage of the Internet has brought about the problem of information overload: the simultaneous presentation of too much information makes it impossible for users to obtain useful parts from them, and the efficiency of information use decreases instead. Many existing search engines and professional data indexes are essentially means to help users filter information. However, traditional search algorithms can only present the same sorting results to all users, and cannot provide corresponding services for different users' hobbies. The personalized recommendation system is based on massive data mining, supported...

AI Technical Summary

Problems solved by technology

[0002] The rapid growth of the scale and coverage of the Internet has brought about the problem of information overload: the simultaneous presentation of too much information makes it impossible for users to obtain useful parts from them, and the efficiency of information use decreases instead.

[0004] However, with the above social recommendation methods, as the number of team members and products increases, the performance of the system will become lower and lower; they are all aimed at the preference discovery of a single team member, so for the situation where there are many team members and complex social relationships, it is recommended accuracy will be greatly reduced

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