Adapting neighborhood and matrix factorization models for context aware recommendation

Abstract

In this paper, we describe our solutions to the weekly recommendation track and social network track of the CAMRA 2010 challenge. The key challenge in the weekly recommendation track is designing models that can cope with time dependent user or item characteristics. Toward this goal, we compared two general approaches, one is a data weighting approach, the other is a time-aware modeling approach. Both approaches can be implemented by extending either the well known neighborhood model or the matrix factorization. For the social network track, we developed and compared two extensions of the matrix factorization models for incorporating the social network structure, namely collective matrix factorization(CMF) and network regularized matrix factorization(NRMF). Experimental results shows that the use of temporal information can lead to significant improvement on the weekly recommendation track whereas for the social network track, the NRMF could lead to minor improvement by combining the social network with the rating data.