Improving Implicit Feedback-Based Recommendation through Multi-Behavior Alignment
Xin Xin, Xiangyuan Liu, Hanbing Wang, Pengjie Ren, Zhumin Chen, Jiahuan Lei, Xinlei Shi, Hengliang Luo, Joemon M. Jose, Maarten de Rijke, Zhaochun Ren
Abstract
Recommender systems that learn from implicit feedback often use large volumes of a single type of implicit user feedback, such as clicks, to enhance the prediction of sparse target behavior such as purchases. Using multiple types of implicit user feedback for such target behavior prediction purposes is still an open question. Existing studies that attempted to learn from multiple types of user behavior often fail to: (i) learn universal and accurate user preferences from different behavioral data distributions, and (ii) overcome the noise and bias in observed implicit user feedback.
Topics & Concepts
Computer scienceRecommender systemHuman–computer interactionNoise (video)Machine learningArtificial intelligenceImage (mathematics)Recommender Systems and TechniquesAdvanced Bandit Algorithms ResearchMobile Crowdsensing and Crowdsourcing