Multi-modal Mixture of Experts Represetation Learning for Sequential Recommendation
Shuqing Bian, Xingyu Pan, Wayne Xin Zhao, Jinpeng Wang, Chuyuan Wang, Ji-Rong Wen
Abstract
Within online platforms, it is critical to capture the dynamic user preference from the sequential interaction behaviors for making accurate recommendation over time. Recently, significant progress has been made in sequential recommendation with deep learning. However, existing neural sequential recommender often suffer from the data sparsity issue in real-world applications.
Topics & Concepts
Computer scienceRecommender systemModalPreferenceArtificial intelligenceDeep learningMachine learningDeep neural networksArtificial neural networkEconomicsMicroeconomicsChemistryPolymer chemistryRecommender Systems and TechniquesExpert finding and Q&A systemsAdvanced Bandit Algorithms Research