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2017, Volume 18, Issue 5

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Frontiers of Information Technology & Electronic Engineering >> 2017, Volume 18, Issue 5 doi: 10.1631/FITEE.1500402

Personalized topic modeling for recommending user-generated content

. State Key Laboratory of Computer Science, Institute of Software, Chinese Academy of Sciences, Beijing 100190, China.. School of Information Science and Engineering, University of Chinese Academy of Sciences, Beijing 100190, China.. Agricultural Information Institute, Chinese Academy of Agricultural Sciences, Beijing 100081, China.. Key Laboratory of Agri-information Service Technology, Ministry of Agriculture, Beijing 100081, China.. 5Water Information Centre, Ministry of Water Resources, Beijing 100053, China

Available online: 2017-06-22
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Abstract

User-generated content (UGC) such as blogs and twitters are exploding in modern Internet services. In such systems, recommender systems are needed to help people filter vast amount of UGC generated by other users. However, traditional rec-ommendation models do not use user authorship of items. In this paper, we show that with this additional information, we can significantly improve the performance of recommendations. A generative model that combines hierarchical topic modeling and matrix factorization is proposed. Empirical results show that our model outperforms other state-of-the-art models, and can provide interpretable topic structures for users and items. Furthermore, since user interests can be inferred from their productions, rec-ommendations can be made for users that do not have any ratings to solve the cold-start problem.

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