Parameter-Efficient Transfer from Sequential Behaviors for User Modeling and Recommendation
Fajie Yuan, Xiangnan He, Alexandros Karatzoglou, Liguang Zhang
Abstract
Inductive transfer learning has had a big impact on computer vision and NLP domains but has not been used in the area of recommender systems. Even though there has been a large body of research on generating recommendations based on modeling user-item interaction sequences, few of them attempt to represent and transfer these models for serving downstream tasks where only limited data exists.
Topics & Concepts
Computer scienceRecommender systemTransfer of learningTransfer (computing)Artificial intelligenceDownstream (manufacturing)Machine learningData modelingHuman–computer interactionNatural language processingInformation retrievalDatabaseParallel computingEconomicsOperations managementRecommender Systems and TechniquesAdvanced Bandit Algorithms ResearchAdvanced Graph Neural Networks