Please use this identifier to cite or link to this item: https://doi.org/10.1145/2009916.2010052
Title: A unified framework for recommendations based on quaternary semantic analysis
Authors: Chen, W.
Hsu, W. 
Lee, M.L. 
Keywords: Collaborative tagging
Personalization
Recommendation
Social tags
Tensor factorization
Issue Date: 2011
Source: Chen, W.,Hsu, W.,Lee, M.L. (2011). A unified framework for recommendations based on quaternary semantic analysis. SIGIR'11 - Proceedings of the 34th International ACM SIGIR Conference on Research and Development in Information Retrieval : 1023-1032. ScholarBank@NUS Repository. https://doi.org/10.1145/2009916.2010052
Abstract: Social network systems such as FaceBook and YouTube have played a significant role in capturing both explicit and implicit user preferences for different items in the form of ratings and tags. This forms a quaternary relationship among users, items, tags and ratings. Existing systems have utilized only ternary relationships such as users-items-ratings, or users-items-tags to derive their recommendations. In this paper, we show that ternary relationships are insufficient to provide accurate recommendations. Instead, we model the quaternary relationship among users, items, tags and ratings as a 4-order tensor and cast the recommendation problem as a multi-way latent semantic analysis problem. A unified framework for user recommendation, item recommendation, tag recommendation and item rating prediction is proposed. The results of extensive experiments performed on a real world dataset demonstrate that our unified framework outperforms the state-of-the-art techniques in all the four recommendation tasks.
Source Title: SIGIR'11 - Proceedings of the 34th International ACM SIGIR Conference on Research and Development in Information Retrieval
URI: http://scholarbank.nus.edu.sg/handle/10635/40242
ISBN: 9781450309349
DOI: 10.1145/2009916.2010052
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