Cold-Start Recommendation for On-Demand Cinemas

Abstract

The on-demand cinemas, which has emerged in recent years, provide offline entertainment venues for individuals and small groups. Because of the limitation of network speed and storage space, it is necessary to recommend movies to cinemas, that is, to suggest cinemas to download the recommended movies in advance. This is particularly true for new cinemas. For the new cinema cold-start recommendation, we build a matrix factorization framework and then fuse location categories of cinemas and co-popular relationship between movies in the framework. Specifically, location categories of cinemas are learned through LDA from the type information of POIs around the cinemas and used to approximate cinema latent representations. Moreover, a SPPMI matrix that reflects co-popular relationship between movies is constructed and factorized collectively with the interaction matrix by sharing the same item latent representations. Extensive experiments on real-world data are conducted. The experimental results show that the proposed approach yields significant improvements over state-of-the-art cold-start recommenders in terms of precision, recall and NDCG.