Applying landmarks to enhance memory-based collaborative filtering
Memory-based Collaborative Filtering (CF) has been a widely used approach for personalised recommendation with considerable success in many applications. An important issue regarding memory-based CF lies in similarity computation: the sparsity of the rating matrix leads to similarity computations ba...
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| Veröffentlicht in: | Information sciences Jg. 513; S. 412 - 428 |
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| Abstract | Memory-based Collaborative Filtering (CF) has been a widely used approach for personalised recommendation with considerable success in many applications. An important issue regarding memory-based CF lies in similarity computation: the sparsity of the rating matrix leads to similarity computations based on few co-rated items between users, resulting in high sensitive predictions. Additionally, the ‘sparse’ similarity computation has high computational cost, due to the dimensionality of the item space. In this paper, we pursue both these issues. We propose a new model to compute similarity by representing users (or items) through their distances to preselected users, named landmarks. Such user modelling allows the introduction of more ratings into similarity computations through transitive relations created by the landmarks. Unlike conventional memory-based CF, the proposal builds a new user space defined by distances to landmarks, avoiding sensitivity in similarity computations. Findings from our experiments show that the proposed modelling achieves better accuracy than the ‘sparse’ similarity representation in all tested datasets, and has also yielded competitive accuracy results against the compared model-based CF algorithms. Furthermore, the proposed implementation has beaten all compared methods in terms of computational performance, becoming a promising alternative to memory-based CF algorithms for large datasets. |
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| AbstractList | Memory-based Collaborative Filtering (CF) has been a widely used approach for personalised recommendation with considerable success in many applications. An important issue regarding memory-based CF lies in similarity computation: the sparsity of the rating matrix leads to similarity computations based on few co-rated items between users, resulting in high sensitive predictions. Additionally, the ‘sparse’ similarity computation has high computational cost, due to the dimensionality of the item space. In this paper, we pursue both these issues. We propose a new model to compute similarity by representing users (or items) through their distances to preselected users, named landmarks. Such user modelling allows the introduction of more ratings into similarity computations through transitive relations created by the landmarks. Unlike conventional memory-based CF, the proposal builds a new user space defined by distances to landmarks, avoiding sensitivity in similarity computations. Findings from our experiments show that the proposed modelling achieves better accuracy than the ‘sparse’ similarity representation in all tested datasets, and has also yielded competitive accuracy results against the compared model-based CF algorithms. Furthermore, the proposed implementation has beaten all compared methods in terms of computational performance, becoming a promising alternative to memory-based CF algorithms for large datasets. |
| Author | Lima, Gustavo R. Lyra, Adria Zimbrao, Geraldo Mello, Carlos E. |
| Author_xml | – sequence: 1 givenname: Gustavo R. orcidid: 0000-0001-5951-2316 surname: Lima fullname: Lima, Gustavo R. email: grlima@cos.ufrj.br organization: PESC, COPPE, Federal University of Rio de Janeiro, Rio de Janeiro 68511, Brazil – sequence: 2 givenname: Carlos E. orcidid: 0000-0002-3632-4002 surname: Mello fullname: Mello, Carlos E. email: mello@uniriotec.br organization: PPGI, Federal University of the State of Rio de Janeiro, Av. Pasteur 458 - Urca, Rio de Janeiro, Brazil – sequence: 3 givenname: Adria surname: Lyra fullname: Lyra, Adria email: adrialyra@ufrrj.br organization: DCC, Federal Rural University of Rio de Janeiro, Nova Iguacu, Rio de Janeiro, Brazil – sequence: 4 givenname: Geraldo surname: Zimbrao fullname: Zimbrao, Geraldo email: zimbrao@cos.ufrj.br organization: PESC, COPPE, Federal University of Rio de Janeiro, Rio de Janeiro 68511, Brazil |
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| Keywords | Dimensionality reduction Landmarks Memory-based algorithms Collaborative filtering Non-linear transformations Recommender system |
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