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2017 | OriginalPaper | Buchkapitel

A Probabilistic Model for the Cold-Start Problem in Rating Prediction Using Click Data

verfasst von : ThaiBinh Nguyen, Atsuhiro Takasu

Erschienen in: Neural Information Processing

Verlag: Springer International Publishing

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Abstract

One of the most efficient methods in collaborative filtering is matrix factorization, which finds the latent vector representations of users and items based on the ratings of users to items. However, a matrix factorization based algorithm suffers from the cold-start problem: it cannot find latent vectors for items to which previous ratings are not available. This paper utilizes click data, which can be collected in abundance, to address the cold-start problem. We propose a probabilistic item embedding model that learns item representations from click data, and a model named EMB-MF, that connects it with a probabilistic matrix factorization for rating prediction. The experiments on three real-world datasets demonstrate that the proposed model is not only effective in recommending items with no previous ratings, but also outperforms competing methods, especially when the data is very sparse.

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Vorheriges Kapitel Discovery of Interconnection Among Knowledge Areas of Standard Computer Science Curricula by a Data Science Approach

Nächstes Kapitel Dynamic Forest Model for Sentiment Classification

The results are obtain by using the LibRec library: http://librec.net/.

Barkan, O., Koenigstein, N.: Item2Vec: neural item embedding for collaborative filtering. In: 26th IEEE International Workshop on Machine Learning for Signal Processing, pp. 1–6 (2016)

Bell, R.M., Koren, Y.: Scalable collaborative filtering with jointly derived neighborhood interpolation weights. In: Proceedings of the 7th IEEE International Conference on Data Mining, pp. 43–52 (2007)

Bullinaria, J.A., Levy, J.P.: Extracting semantic representations from word co-occurrence statistics: a computational study. Behav. Res. Methods, 510–526 (2007)

Church, K.W., Hanks, P.: Word association norms, mutual information, and lexicography. Comput. Linguist. 22–29 (1990)

Gopalan, P., Hofman, J.M., Blei, D.M.: Scalable recommendation with hierarchical Poisson factorization. In: Proceedings of the 31st Conference on Uncertainty in Artificial Intelligence, pp. 326–335 (2015)

Gopalan, P.K., Charlin, L., Blei, D.: Content-based recommendations with Poisson factorization. In: Proceedings of the 27th Advances in Neural Information Processing Systems, pp. 3176–3184 (2014)

Hu, Y., Koren, Y., Volinsky, C.: Collaborative filtering for implicit feedback datasets. In: Proceedings of the 8th IEEE International Conference on Data Mining, pp. 263–272 (2008)

Koren, Y.: Factorization meets the neighborhood: a multifaceted collaborative filtering model. In: Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 426–434 (2008)

Levy, O., Goldberg, Y.: Neural word embedding as implicit matrix factorization. In: Proceedings of the 27th International Conference on Neural Information Processing Systems, pp. 2177–2185 (2014)

10.

Liang, D., Altosaar, J., Charlin, L., Blei, D.M.: Factorization meets the item embedding: regularizing matrix factorization with item co-occurrence. In: Proceedings of the 10th ACM Conference on Recommender Systems, pp. 59–66 (2016)

11.

Liu, N.N., Xiang, E.W., Zhao, M., Yang, Q.: Unifying explicit and implicit feedback for collaborative filtering. In: Proceedings of the 19th ACM International Conference on Information and Knowledge Management, pp. 1445–1448 (2010)

12.

Mnih, A., Salakhutdinov, R.R.: Probabilistic matrix factorization. In: 20th Advances in Neural Information Processing Systems, pp. 1257–1264 (2008)

13.

Wang, B., Rahimi, M., Zhou, D., Wang, X.: Expectation-maximization collaborative filtering with explicit and implicit feedback. In: Tan, P.-N., Chawla, S., Ho, C.K., Bailey, J. (eds.) PAKDD 2012. LNCS, vol. 7301, pp. 604–616. Springer, Heidelberg (2012). doi:10.1007/978-3-642-30217-6_50 CrossRef

14.

Wang, C., Blei, D.M.: Collaborative topic modeling for recommending scientific articles. In: Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 448–456 (2011)

15.

van den Oord, A., Dieleman, S., Schrauwen, B.: Deep content-based music recommendation. In: Proceedings of the Advances in Neural Information Processing Systems, vol. 26, pp. 2643–2651 (2013)

Titel: A Probabilistic Model for the Cold-Start Problem in Rating Prediction Using Click Data
verfasst von: ThaiBinh Nguyen
Atsuhiro Takasu
Verlag: Springer International Publishing
Buch: Neural Information Processing
Print ISBN: 978-3-319-70138-7

Electronic ISBN: 978-3-319-70139-4

Copyright-Jahr: 2017
DOI: https://doi.org/10.1007/978-3-319-70139-4_20

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