Hybrid Q-Learning Applied to Ubiquitous recommender system

TL;DR

This paper introduces a hybrid reinforcement learning and case-based reasoning approach for ubiquitous recommender systems, aiming to improve user acceptance and recommendation quality across multiple context dimensions.

Contribution

It presents a novel combination of reinforcement learning and case-based reasoning inspired by human reasoning models for context-aware recommendations.

Findings

Increased recommendation quality demonstrated in preliminary experiments.

Effective modeling of social, temporal, and geographic contexts.

Proposed framework outperforms traditional methods.

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.