藉由資料導向的特徵轉換改進流形正則化單類別協同過濾模型

連彥傑

doi:10.6342/NTU201601737

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藉由資料導向的特徵轉換改進流形正則化單類別協同過濾模型

Improving One-class Collaborative Filtering with Manifold Regularization by Data-driven Feature Transformation

連彥傑 , Masters　　Advisor：鄭卜壬　　

英文 DOI： 10.6342/NTU201601737

流形正則化；特徵轉換；單類別協同過濾；推薦系統；貝氏個人化排序； Manifold Regularization ； Feature Transformation ； One-class Collaborative Filtering ； Recommender System ； Bayesian Personalized Ranking

Abstract │ Reference (15) │ Altmetrics

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Manifold Regularization is introduced to the field of recommender system for combining additional features into collaborative filtering. However, most works choose features to incorporate intuitively or by domain knowledge. Besides, the effect of feature combination needs to be considered. Although some methods of dimension reduction help to transform raw features to latent space with combination for usage, these approaches cannot give an appropriate representation for the problem of recommendation because they are based only on the semantic of features. In this work, we design a data-driven framework for training of transformation function. For the idea of manifold regularization that similar features bring similar behavior, our approach uses   an item-based method and a ranking framework to optimize the representation function of item's feature from user's feedback data. In the experiment, we show that for manifold regularized model, the transformed features through the proposed method can boost performance better for recommendation problem compared with raw feature and semantic-based transformed features. Besides, we also show our framework's capability of detecting quality of feature for recommendation task.

Reference ( 15 ) 〈TOP〉

[1] M. Belkin, P. Niyogi, and V. Sindhwani. Manifold regularization: A geometric framework for learning from labeled and unlabeled examples. Journal of Machine Learning Research, 7:2399–2434, 2006.
連結：
[2] L. Du, X. Li, and Y.-D. Shen. User graph regularized pairwise matrix factorization for item recommendation. In Proceedings of the 7th International Conference on Advanced Data Mining and Applications - Volume Part II, ADMA’11, pages 372–385, 2011.
連結：
[3] Y. Gu, B. Zhao, D. Hardtke, and Y. Sun. Learning global term weights for content-based recommender systems. In Proceedings of the 25th International Conference on World Wide Web, WWW ’16, pages 391–400. International World Wide Web Conferences Steering Committee, 2016.
連結：
[4] R. He and J. McAuley. VBPR: visual bayesian personalized ranking from implicit feedback. In AAAI Conference on Artificial Intelligence, 2016.
連結：
[6] I. Jolliffe. Principal component analysis. Wiley Online Library, 2002.
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