Who are like-minded

mining user interest similarity in online social networks

Chunfeng Yang, Yipeng Zhou, Dah Ming Chiu

Research output: Chapter in Book/Report/Conference proceedingConference proceeding contribution

4 Citations (Scopus)

Abstract

In this paper, we mine and learn to predict how similar a pair of users’ interests towards videos are, based on demographic, social and interest information of these users. We use the video access patterns of active users as ground truth. We adopt tag-based user profiling to establish this ground truth. We then show the effectiveness of the different features, and their combinations and derivatives, in predicting user interest similarity, based on different machine-learning methods for combining multiple features. We propose a hybrid tree-encoded linear model for combining the features, and show that it out-performs other linear and tree-based models. Our methods can be used to predict user interest similarity when the ground-truth is not available, e.g. for new users, or inactive users whose interests may have changed from old access data, and is useful for video recommendation.
Original languageEnglish
Title of host publicationTenth International AAAI Conference on Web and Social Media
PublisherAssociation for the Advancement of Artificial Intelligence
Pages731-734
Number of pages4
ISBN (Electronic)9781577357582
Publication statusPublished - 2016
Externally publishedYes
Event10th International Conference on Web and Social Media, ICWSM 2016 - Cologne, Germany
Duration: 17 May 201620 May 2016

Conference

Conference10th International Conference on Web and Social Media, ICWSM 2016
CountryGermany
CityCologne
Period17/05/1620/05/16

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  • Cite this

    Yang, C., Zhou, Y., & Chiu, D. M. (2016). Who are like-minded: mining user interest similarity in online social networks. In Tenth International AAAI Conference on Web and Social Media (pp. 731-734). Association for the Advancement of Artificial Intelligence.