Learning rating patterns for Top-N recommendations
Contribuinte(s) |
[Unknown] |
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Data(s) |
01/01/2012
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Resumo |
Two rating patterns exist in the user × item rating matrix and influence each other: the personal rating patterns are hidden in each user's entire rating history, while the global rating patterns are hidden in the entire user × item rating matrix. In this paper, a Rating Pattern Subspace is proposed to model both of the rating patterns simultaneously by iteratively refining each other with an EM-like algorithm. Firstly, a low-rank subspace is built up to model the global rating patterns from the whole user × item rating matrix, then, the projection for each user on the subspace is refined individually based on his/her own entire rating history. After that, the refined user projections on the subspace are used to improve the modelling of the global rating patterns. Iteratively, we can obtain a well-trained low-rank Rating Pattern Subspace, which is capable of modelling both the personal and the global rating patterns. Based on this subspace, we propose a RapSVD algorithm to generate Top-N recommendations, and the experiment results show that the proposed method can significantly outperform the other state-of-the-art Top-N recommendation methods in terms of accuracy, especially on long tail item recommendations. |
Identificador | |
Idioma(s) |
eng |
Publicador |
IEEE Computer Society |
Relação |
http://dro.deakin.edu.au/eserv/DU:30051354/evid-asonamconf-2012.pdf http://dro.deakin.edu.au/eserv/DU:30051354/evid-asonampeerrevwgnrl-2012.pdf http://dro.deakin.edu.au/eserv/DU:30051354/ren-learningrating-2012.pdf http://dx.doi.org/10.1109/ASONAM.2012.81 |
Palavras-Chave | #rating patterns #Top-N recommendations |
Tipo |
Conference Paper |