首页|Personalized topic modeling for recommending user-generated content

Personalized topic modeling for recommending user-generated content

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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.

User-generated content (UGC)Collaborative filtering (CF)Matrix factorization (MF)Hierarchical topic modeling

Wei ZHANG、Jia-yu ZHUANG、Xi YONG、Jian-kou LI、Wei CHEN、Zhe-min LI

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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

Water Information Centre, Ministry of Water Resources, Beijing 100053, China

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Project supported by the Monitoring Statistics Project on Agricultural and Rural Resources, MOA, ChinaInnovative Talents Project,MOA, ChinaScience and Technology Innovation Project Fund of Chinese Academy of Agricultural Sciences

CAASASTIP-2015-AI I-02

2017

信息与电子工程前沿(英文)
浙江大学

信息与电子工程前沿(英文)

SCIEI
影响因子：0.371
ISSN：2095-9184
年,卷(期)：2017.18(5)
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