Beyond User Embedding Matrix
Shaoyun Shi, Weizhi Ma, Min Zhang, Yongfeng Zhang, Xinxing Yu, Houzhi Shan, Yiqun Liu, Shaoping Ma
Abstract
Modeling large scale and rare-interaction users are the two major challenges in recommender systems, which derives big gaps between researches and applications. Facing to millions or even billions of users, it is hard to store and leverage personalized preferences with a user embedding matrix in real scenarios. And many researches pay attention to users with rich histories, while users with only one or several interactions are the biggest part in real systems. Previous studies make efforts to handle one of the above issues but rarely tackle efficiency and cold-start problems together.
Topics & Concepts
Leverage (statistics)Computer scienceRecommender systemEmbeddingCollaborative filteringData scienceHuman–computer interactionWorld Wide WebArtificial intelligenceRecommender Systems and TechniquesAdvanced Bandit Algorithms ResearchConsumer Market Behavior and Pricing