Learning Higher-Order Interactions for User and Item Profiling Based on Tensor Factorization
Xiaoyu Tang, Yue Xu, Shlomo Geva · 2015
User profiling techniques play a central role in many Recommender Systems (RS). In recent years, multidimensional data are getting increasing attention for making recommendations. Additional metadata help algorithms better understanding users' behaviors and decisions. Existing user/item profiling techniques for Collaborative Filtering (CF) RS in multidimensional environment mostly analyze data through splitting the multidimensional relations. However, this leads to the loss of multidimensionality in user-item interactions; whereas the interactions are naturally multidimensional since users' choices are often affected by contextual information. In this paper, we propose a unified profiling approach which models users/items with latent higher-order interaction factors. We demonstrate that the proposed profiling approach is intimately related to two-dimensional profiling based on Matrix Factorization techniques. We further propose to integrate the profiling approach into three neighborhood-based CF recommenders for item recommendation. Finally, we empirically show on real-world social tagging datasets that the proposed recommenders outperform state-of-the-art CF recommendation approaches in accuracy.