Sequential Recommendation with Self-Attentive Multi-Adversarial Network
Ruiyang Ren, Zhaoyang Liu, Yaliang Li, Wayne Xin Zhao, Hui Wang, Bolin Ding, Ji-Rong Wen
Abstract
Recently, deep learning has made significant progress in the task of sequential recommendation. Existing neural sequential recommenders typically adopt a generative way trained with Maximum Likelihood Estimation (MLE). When context information (called factor) is involved, it is difficult to analyze when and how each individual factor would affect the final recommendation performance.
Topics & Concepts
Computer scienceArtificial intelligenceTask (project management)Context (archaeology)Artificial neural networkMachine learningDeep learningEstimationGenerative modelRecommender systemData miningGenerative grammarMaximum likelihoodAffect (linguistics)Complete informationDeep neural networksFactor (programming language)Sequential estimationSequence learningTraining setGenerative Adversarial Networks and Image SynthesisMachine Learning in HealthcareRecommender Systems and Techniques