Repetition and Exploration in Sequential Recommendation
Ming Li, Ali Vardasbi, Andrew Yates, Maarten de Rijke
Abstract
In several recommendation scenarios, including next basket recommendation, the importance of repetition and exploration has been discovered and studied. Sequential recommenders (SR) aim to infer a user's preferences and suggest the next item for them to interact with based on their historical interaction sequences. There has not been a systematic analysis of sequential recommenders from the perspective of repetition and exploration. As a result, it is unclear how these models, that are typically optimized for accuracy, perform in terms of repetition and exploration, as well as the potential drawbacks of deploying them in real applications.
Topics & Concepts
Repetition (rhetorical device)Computer sciencePerspective (graphical)Recommender systemArtificial intelligenceMachine learningLinguisticsPhilosophyRecommender Systems and TechniquesAdvanced Bandit Algorithms ResearchAdvanced Graph Neural Networks