Abstract
In many recommendation applications, like music and movies recommendation, describing the features of items heavily relies on user-generated contents, especially social tags. They suffer from serious problems including redundancy and self-contradiction. Direct exploitation of them in a recommender system leads to reduced performance. However, few systems have taken this problem into consideration.
In this paper, we propose a novel framework named as prior knowledge based context aware recommender (PKCAR). We incorporate Dirichlet Forrest priors to encode prior knowledge about item features into our model to deal with the redundancy, and self-contradiction problems. We also develop an algorithm which automatically mine prior knowledge using co-occurrence, lexical and semantic features. We evaluate our framework on two datasets from different domains. Experimental results show that our approach performs better than systems without leveraging prior knowledge about item features.
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References
Adomavicius, G., Tuzhilin, A.: Context-aware recommender systems. In: Proceedings of the 2008 ACM Conference on Recommender Systems, RecSys 2008, p. 335 (2008)
Andrzejewski, D., Zhu, X., Craven, M.: Incorporating domain knowledge into topic modeling via dirichlet forest priors (2009)
Baltrunas, L., Ricci, F.: Context-based splitting of item ratings in collaborative filtering. In: Proceedings of the Third ACM Conference on Recommender Systems, RecSys 2009, p. 245 (2009)
Chen, S., Moore, J.L., Turnbull, D., Joachims, T.: Playlist prediction via metric embedding. In: Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2012, p. 714. ACM Press, New York (2012)
Chen, S., Xu, J., Joachims, T.: Multi-space probabilistic sequence modeling. In: Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2013, p. 865. ACM Press, New York (2013)
Chen, Z., Mukherjee, A., Liu, B., Hsu, M., Castellanos, M., Ghosh, R.: Leveraging multi-domain prior knowledge in topic models, pp. 2071–2077 (2013)
Hariri, N., Mobasher, B., Burke, R.: Query-driven context aware recommendation. In: Proceedings of the 7th ACM Conference on Recommender Systems, RecSys 2013, pp. 9–16. ACM Press, New York (2013)
Said, A., De Luca, E.W., Albayrak, S.: Inferring contextual user profiles improving recommender performance, vol. 791. CEUR Workshop Proceedings, Chicago (2011)
Acknowledgements
This research is supported by National Natural Science Foundation of China (Grant No. 61375054 and 61402045), Natural Science Foundation of Guangdong Province (Grant No. 2014A030313745), Tsinghua University Initiative Scientific Research Program (Grant No.20131089256), and Cross fund of Graduate School at Shenzhen, Tsinghua University (Grant No. JC20140001).
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Zheng, H., Mao, X. (2016). Incorporating Prior Knowledge into Context-Aware Recommendation. In: Hirose, A., Ozawa, S., Doya, K., Ikeda, K., Lee, M., Liu, D. (eds) Neural Information Processing. ICONIP 2016. Lecture Notes in Computer Science(), vol 9949. Springer, Cham. https://doi.org/10.1007/978-3-319-46675-0_55
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DOI: https://doi.org/10.1007/978-3-319-46675-0_55
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