Social network friend recommendation method based on community discovery

Abstract

The invention discloses a social network friend recommendation method based on community discovery, belongs to the fields of data mining, social networks and the like, and aims at solving the problem that the social network characteristic is not taken into account in individual recommendation. The method comprises the following steps: acquiring user history data in the social network, modeling hobbies and interests of users to obtain a preference vector set of all users, clustering the users according to the preference vectors representing the hobbies and the interests of the users and the preference vectors representing the hobbies and the interests of friends of the users in the user preference vector set, finding an overlap area of the hobbies and the interests of the users and the friends in the social network, acquiring an initial target user list of friends to be recommended according to the overlap area of the hobbies and the interests of the users and the friends, filtering and sequencing the obtained initial target user list of friends to be recommended, thereby obtaining a final list of friends to be recommended. As friends are recommended according to the hobbies and the interests and the friend relationship of the users, the method is more applicable to social networks.

Description

technical field [0001] A social network friend recommendation method based on community discovery, which uses the typical complex network characteristics of social networks to find the user's hobbies and hobbies in the social network, uses the user's hobbies and friend relationships to recommend friends, and improves the accuracy of personalized recommendation User experience of degree and social services, involving data mining, social network, personalized recommendation and other fields. Background technique [0002] Social network is a typical complex network, which has small-world, scale-free and community structure characteristics of complex networks. [0003] The small-world property means that in the case of a large-scale network, any two network nodes can establish a connection through relatively small steps. The social network not only fully conforms to the "six degrees of separation" theory, but also has a small characteristic path length, which conforms to the sm...

AI Technical Summary

Problems solved by technology

[0009] Existing community discovery algorithms usually only use the network topology graph of user-friend relationships in social networks, and the communities found are non-overlapping communities

In addition, the existing friend recommendation methods often only use the user's friend relationship, without considering the user's hobbies or the dynamic changes of the user's hobbies.

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