Exploring Longitudinal Effects of Session-based Recommendations
Andrés Ferraro, Dietmar Jannach, Xavier Serra
Abstract
Session-based recommendation is a problem setting where the task of a recommender system is to make suitable item suggestions based only on a few observed user interactions in an ongoing session. The lack of long-term preference information about individual users in such settings usually results in a limited level of personalization, where a small set of popular items may be recommended to many users. This repeated exposure of such a subset of the items through the recommendations may in turn lead to a reinforcement effect over time, and to a system which is not able to help users discover new content anymore to the desirable extent.
Topics & Concepts
Session (web analytics)PersonalizationComputer scienceTask (project management)Recommender systemPreferenceSet (abstract data type)Term (time)MultimediaInformation retrievalHuman–computer interactionWorld Wide WebEngineeringProgramming languageEconomicsMicroeconomicsPhysicsQuantum mechanicsSystems engineeringRecommender Systems and TechniquesAdvanced Bandit Algorithms ResearchConsumer Market Behavior and Pricing