Two-level matrix factorization for recommender systems
- Springer Verlag (Germany)
- Publication Type:
- Journal Article
- Neural Computing and Applications, 2016, 27 (8), pp. 2267 - 2278
- Issue Date:
Many existing recommendation methods such as matrix factorization (MF) mainly rely on user–item rating matrix, which sometimes is not informative enough, often suffering from the cold-start problem. To solve this challenge, complementary textual relations between items are incorporated into recommender systems (RS) in this paper. Specifically, we first apply a novel weighted textual matrix factorization (WTMF) approach to compute the semantic similarities between items, then integrate the inferred item semantic relations into MF and propose a two-level matrix factorization (TLMF) model for RS. Experimental results on two open data sets not only demonstrate the superiority of TLMF model over bench-mark methods, but also show the effectiveness of TLMF for solving the cold-start problem.
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