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Neural Tensor Model for Learning Multi-Aspect Factors in Recommender Systems
Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence
Recommender systems often involve multi-aspect factors. For example, when shopping for shoes online, consumers usually look through their images, ratings, and product's reviews before making their decisions. To learn multi-aspect factors, many context-aware models have been developed based on tensor factorizations. However, existing models assume multilinear structures in the tensor data, thus failing to capture nonlinear feature interactions. To fill this gap, we propose a novel nonlineardoi:10.24963/ijcai.2020/335 dblp:conf/ijcai/WangL20 fatcat:re35ls7675bqdo5efyld3uofse