Method and system for recommending friend information in social network

Abstract

The invention provides a method and system for recommending friend information in the social network. The method comprises the following steps that firstly, after recommending request information of a user is received, a social network information set is established according to operation behavior information and attribute information of users in the social network; secondly, a friend community set including the user and other K users is found in the social network information set; thirdly, behavior orientation information of the other K users in the friend community set is analyzed and recommended to the user. According to the method and system for recommending the friend information in the social network, the information of obvious friends is analyzed in a recommended result, the preference and influence of hidden friends of the user are also taken into consideration, and compared with an existing scheme, the method and system have the advantage that the produced recommended result is further improved on the aspect of precision.

Description

technical field [0001] The invention relates to the field of business support, in particular to a method and system for recommending friend information in a social network. Background technique [0002] Existing social networks are very popular with Internet users, because users have made many like-minded friends on them, and there are many topics of interest and the same hobbies, which make them stick to a certain extent. Recommendation information from friends is very valuable. The recommendation system used in the prior art guesses the needs of the user by analyzing the user (mainly from the two aspects of the interaction history between the user and the recommendation system and the user's personal information), and can provide the user with interesting content. [0003] In the prior art, recommendation systems are generally divided into three categories: collaborative filtering-based methods (Collaborative Filtering, CF), content-based methods (Content-based Filitering...

AI Technical Summary

Problems solved by technology

And if the user does not leave historical data, we cannot accurately discover the "common interests" between him and other users

Therefore, the memory-based method mainly has the following defects: the data sparseness of the recommendation information leads to poor recommendation accuracy and poor scalability

[0004] Generally speaking, most of the existing social network recommendation technologies are based on the user's explicit attributes, and the mining effect on the current social network friend community collection is relatively poor, and the recommendation information is not comprehensive enough. Therefore, for the existing technologies, how to efficiently and accurately It is a difficult problem to mine the user's friend community collection from the social network and recommend more accurate and comprehensive information to the user.

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