NDLI: Context adaptation in interactive recommender systems

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Context adaptation in interactive recommender systems

Content Provider	ACM Digital Library
Author	Burke, Robin Mobasher, Bamshad Hariri, Negar
Abstract	Contextual factors can greatly influence the utility of recommendations for users. In many recommendation and personalization applications, particularly in domains where user context changes dynamically, it is difficult to represent and model contextual factors directly, but it is often possible to observe their impact on user preferences during the course of users' interactions with the system. In this paper, we introduce an interactive recommender system that can detect and adapt to changes in context based on the user's ongoing behavior. The system, then, dynamically tailors its recommendations to match the user's most recent preferences. We formulate this problem as a multi-armed bandit problem and use Thompson sampling heuristic to learn a model for the user. Following the Thompson sampling approach, the user model is updated after each interaction as the system observes the corresponding rewards for the recommendations provided during that interaction. To generate contextual recommendations, the user's preference model is monitored for changes at each step of interaction with the user and is updated incrementally. We will introduce a mechanism for detecting significant changes in the user's preferences and will describe how it can be used to improve the performance of the recommender system.
Starting Page	41
Ending Page	48
Page Count	8
File Format	PDF MP4
ISBN	9781450326681
DOI	10.1145/2645710.2645753
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2014-10-06
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Collaborative filtering Thompson sampling Context-aware recommendation Multi-armed bandit problem
Content Type	Audio Text
Resource Type	Article

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