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- W2766881452 abstract "Friend recommendation has been one of the most challenging problems as the social networks grow rapidly, due to the needs of seeking people who are acquaintances in real life or share the common interests. In this paper, we tackle the problem by treating it as a link prediction task and propose a hybrid algorithm that exploits the existing friendship links, users' history ratings and the tags annotated to items. Specifically, we identify the underlying groups in the social network based on different aspects and draw the features that express the involvement degree of the users to the groups. The features are then incorporated into a user intention model, in order to learn some general characteristics about how much users inside(between) a group like to associate with others and how much users like to associate with common-interest people. The proposed algorithm is evaluated on a real-world dataset called LibraryThing. The experimental results show that prediction accuracy of our algorithm is improved remarkably compared to the standard algorithms." @default.
- W2766881452 created "2017-11-10" @default.
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- W2766881452 date "2017-03-01" @default.
- W2766881452 modified "2023-10-17" @default.
- W2766881452 title "A user intention modeling algorithm for friend recommendation" @default.
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- W2766881452 doi "https://doi.org/10.1109/icbda.2017.8078745" @default.
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