TY - JOUR
T1 - Two-level matrix factorization for recommender systems
AU - Li, Fangfang
AU - Xu, Guandong
AU - Cao, Longbing
PY - 2016/11
Y1 - 2016/11
N2 - 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 benchmark methods, but also show the effectiveness of TLMF for solving the cold-start problem.
AB - 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 benchmark methods, but also show the effectiveness of TLMF for solving the cold-start problem.
KW - Latent factor model
KW - Matrix factorization
KW - Recommender system
KW - Textual semantic relation
UR - http://www.scopus.com/inward/record.url?scp=84940843065&partnerID=8YFLogxK
UR - http://purl.org/au-research/grants/arc/DP1096218
UR - http://purl.org/au-research/grants/arc/DP130102691
UR - http://purl.org/au-research/grants/arc/LP100200774
U2 - 10.1007/s00521-015-2060-3
DO - 10.1007/s00521-015-2060-3
M3 - Article
SN - 0941-0643
VL - 27
SP - 2267
EP - 2278
JO - Neural Computing and Applications
JF - Neural Computing and Applications
IS - 8
ER -