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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Project supported by the Monitoring Statistics Project on Agricul-tural and Rural Resources, MOA, China, the Innovative Talents Pro-ject, MOA, China, and the Science and Technology Innovation Project Fund of Chinese Academy of Agricultural Sciences (No. CAAS- ASTIP-2015-AI I-02)
ORCID: Zhe-min LI, http://orcid.org/0000-0003-3170-0117
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Zhang, W., Zhuang, Jy., Yong, X. et al. Personalized topic modeling for recommending user-generated content. Frontiers Inf Technol Electronic Eng 18, 708–718 (2017). https://doi.org/10.1631/FITEE.1500402
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DOI: https://doi.org/10.1631/FITEE.1500402
Key words
- User-generated content (UGC)
- Collaborative filtering (CF)
- Matrix factorization (MF)
- Hierarchical topic modeling