article

Discerning individual interests and shared interests for social user profiling

Authors:

Guangxiang Zeng,

Hui XiongAuthors Info & Claims

World Wide Web, Volume 20, Issue 2

Pages 417 - 435

https://doi.org/10.1007/s11280-016-0397-x

Published: 01 March 2017 Publication History

Abstract

Traditionally, research about social user profiling assumes that users share some similar interests with their followees. However, it lacks the studies on what topic and to what extent their interests are similar. Our study in online sharing sites reveals that besides shared interests between followers and followees, users do maintain some individual interests which differ from their followees. Thus, for better social user profiling we need to discern individual interests (capturing the uniqueness of users) and shared interests (capturing the commonality of neighboring users) of the users in the connected world. To achieve this, we extend the matrix factorization model by incorporating both individual and shared interests, and also learn the multi-faceted similarities unsupervisedly. The proposed method can be applied to many applications, such as rating prediction, item level social influence maximization and so on. Experimental results on real-world datasets show that our work can be applied to improve the performance of social rating. Also, it can reveal some interesting findings, such as who likes the "controversial" items most, and who is the most influential in attracting their followers to rate an item.

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Cited By

Jin BCheng KZhang LFu YYin MJiang Ld'Aquin MDietze SHauff CCurry ECudre Mauroux P(2020)Partial Relationship Aware Influence Diffusion via a Multi-channel Encoding Scheme for Social RecommendationProceedings of the 29th ACM International Conference on Information & Knowledge Management10.1145/3340531.3412016(585-594)Online publication date: 19-Oct-2020
https://dl.acm.org/doi/10.1145/3340531.3412016
Unger MShapira BRokach LBar ABielikova MHerder ECena FDesmarais M(2017)Inferring Contextual Preferences Using Deep Auto-EncodingProceedings of the 25th Conference on User Modeling, Adaptation and Personalization10.1145/3079628.3079666(221-229)Online publication date: 9-Jul-2017
https://dl.acm.org/doi/10.1145/3079628.3079666

Discerning individual interests and shared interests for social user profiling
1. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals

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cover image World Wide Web

World Wide Web Volume 20, Issue 2

March 2017

297 pages

ISSN:1386-145X

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Copyright © Copyright © 2017 Springer Science+Business Media New York.

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Kluwer Academic Publishers

United States

Publication History

Published: 01 March 2017

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Cited By

Jin BCheng KZhang LFu YYin MJiang Ld'Aquin MDietze SHauff CCurry ECudre Mauroux P(2020)Partial Relationship Aware Influence Diffusion via a Multi-channel Encoding Scheme for Social RecommendationProceedings of the 29th ACM International Conference on Information & Knowledge Management10.1145/3340531.3412016(585-594)Online publication date: 19-Oct-2020
https://dl.acm.org/doi/10.1145/3340531.3412016
Unger MShapira BRokach LBar ABielikova MHerder ECena FDesmarais M(2017)Inferring Contextual Preferences Using Deep Auto-EncodingProceedings of the 25th Conference on User Modeling, Adaptation and Personalization10.1145/3079628.3079666(221-229)Online publication date: 9-Jul-2017
https://dl.acm.org/doi/10.1145/3079628.3079666

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