Social-aware VR configuration recommendation via multi-feedback coupled tensor factorization

Recent technological advent in virtual reality (VR) has attracted a lot of attention to the VR shopping, which thus far is designed for a single user. In this paper, we envision the scenario of VR group shopping, where VR supports: 1) flexible display of items to address diverse personal preferences, and 2) convenient view switching between personal and group views to foster social interactions. We formulate the Multiview-Enabled Configuration Recommendation (MECR) problem to rank a set of displayed items for a VR shopping user. We design the Multiview-Enabled Configuration Ranking System (MEIRS) that first extracts discriminative features based on Marketing theories and then introduces a new coupled tensor factorization model to learn the representation of users, MultiView Display (MVD) configurations, and multiple feedback with content features. Experimental results manifest that the proposed approach outperforms personalized recommendations and group recommendations by at least 30.8% in large-scale datasets and 63.3% in the user study in terms of hit ratio and mean average precision.