TY - JOUR
T1 - NLMF
T2 - NonLinear matrix factorization methods for top-N recommender systems
AU - Kabbur, Santosh
AU - Karypis, George
PY - 2015/1/1
Y1 - 2015/1/1
N2 - Many existing state-of-the-art top-N recommendation methods model users and items in the same latent space and the recommendation scores are computed via the dot product between those vectors. These methods assume that the user preference is consistent across all the items that he/she has rated. This assumption is not necessarily true, since many users can have multiple personas/interests and their preferences can vary with each such interest. To address this, a recently proposed method modeled the users with multiple interests. In this paper, we build on this approach and model users using a much richer representation. We propose a method which models the user preference as a combination of having global preference and interest-specific preference. The proposed method uses a nonlinear model for predicting the recommendation score, which is used to perform top-N recommendation task. The recommendation score is computed as a sum of the scores from the components representing global preference and interest-specific preference. A comprehensive set of experiments on multiple datasets show that the proposed model outperforms other state-of-the-art methods for top-N recommendation task.
AB - Many existing state-of-the-art top-N recommendation methods model users and items in the same latent space and the recommendation scores are computed via the dot product between those vectors. These methods assume that the user preference is consistent across all the items that he/she has rated. This assumption is not necessarily true, since many users can have multiple personas/interests and their preferences can vary with each such interest. To address this, a recently proposed method modeled the users with multiple interests. In this paper, we build on this approach and model users using a much richer representation. We propose a method which models the user preference as a combination of having global preference and interest-specific preference. The proposed method uses a nonlinear model for predicting the recommendation score, which is used to perform top-N recommendation task. The recommendation score is computed as a sum of the scores from the components representing global preference and interest-specific preference. A comprehensive set of experiments on multiple datasets show that the proposed model outperforms other state-of-the-art methods for top-N recommendation task.
KW - Data mining
KW - Database Applications
KW - Mining methods and algorithms
KW - Personalization
UR - http://www.scopus.com/inward/record.url?scp=84936877854&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84936877854&partnerID=8YFLogxK
U2 - 10.1109/ICDMW.2014.108
DO - 10.1109/ICDMW.2014.108
M3 - Article
AN - SCOPUS:84936877854
SN - 2375-9232
VL - 2015-January
SP - 167
EP - 174
JO - IEEE International Conference on Data Mining Workshops, ICDMW
JF - IEEE International Conference on Data Mining Workshops, ICDMW
IS - January
M1 - 7022594
ER -