Hybrid Q-Learning Applied to Ubiquitous recommender system

Abstract

Ubiquitous information access becomes more and more important nowadays and research is aimed at making it adapted to users. Our work consists in applying machine learning techniques in order to bring a solution to some of the problems concerning the acceptance of the system by users. To achieve this, we propose a fundamental shift in terms of how we model the learning of recommender system: inspired by models of human reasoning developed in robotic, we combine reinforcement learning and case-base reasoning to define a recommendation process that uses these two approaches for generating recommendations on different context dimensions (social, temporal, geographic). We describe an implementation of the recommender system based on this framework. We also present preliminary results from experiments with the system and show how our approach increases the recommendation quality.

arXiv admin note: substantial text overlap with arXiv:1301.4351, arXiv:1303.2308

References in corpus (4)

• Empirical Analysis of Predictive Algorithms for Collaborative Filtering
• Situation-Aware Approach to Improve Context-based Recommender System
• Optimizing an Utility Function for Exploration / Exploitation Trade-off in Context-Aware Recommender System
• Role of temporal inference in the recognition of textual inference

Cited by in corpus (1)

• Etat de l'art sur l'application des bandits multi-bras