Cluster-Based Smoothing and Linear-Function Fusion for Collaborative Filtering

Li, Sun; Hao, Guoqing; Li, Jiyun; Lv, Juntao

doi:10.1007/978-3-642-54924-3_64

Cited by 1 publication

(1 citation statement)

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“…For example, a large number of studies have enhanced the effectiveness of CF by overcoming the sparsity and new item problems (Ceylan and Birturk 2011;Hu and Zhou 2008;Huang et al 2004;Kim et al 2010;Leung et al 2008;Li et al 2007;Lops et al 2007;Mobasher et al 2004;Moshfeghi et al 2011;Shambour and Lu 2011;Sieg et al 2010;Sun et al 2011;Weng et al 2008). Some other studies have attempted to improve the quality of CF by addressing sparsity and scalability issues (Bakshi et al 2014;Cho and Kim 2004;Gong 2010;Koenigstein and Koren 2013;Kumar and Sharma 2012;Sun et al 2014;Verma et al 2013;Xue et al 2005) or new user problem (Ahn 2008;Kim et al 2010;Lam et al 2008;Lika et al 2014;Liu et al 2014;Rodríguez et al 2010).…”

Section: Introductionmentioning

confidence: 99%

New Recommender Framework: Combining Semantic Similarity Fusion and Bicluster Collaborative Filtering

Gohari

Tarokh

2015

Computational Intelligence

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Collaborative filtering (CF) systems help address information overload, by using the preferences of users in a community to make personal recommendations for other users. The widespread use of these systems has exposed some well-known limitations, such as sparsity, scalability, and cold-start, which can lead to poor recommendations. During the last years, a great number of works have focused on the improvement of CF, but they do not solve all its problems efficiently. In this article, we present a new approach that applies semantic similarity fusion as well as biclustering to alleviate the aforementioned problems. The experimental results verify the effectiveness and efficiency of our approach over the benchmark CF methods.

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