The Impact of Situation Clustering in Contextual-Bandit Algorithm for Context-Aware Recommender Systems

TL;DR

This paper presents a novel approach to improve context-aware recommender systems by incorporating situation clustering into a contextual bandit framework, effectively addressing user content dynamicity.

Contribution

It introduces a new algorithm that models CRS as a contextual bandit with situation clustering, enhancing recommendation precision amidst dynamic user content.

Findings

Improved recommendation accuracy through situation clustering.

Effective handling of user content dynamicity.

Validated results with real online event log data.

Abstract

Most existing approaches in Context-Aware Recommender Systems (CRS) focus on recommending relevant items to users taking into account contextual information, such as time, location, or social aspects. However, few of them have considered the problem of user's content dynamicity. We introduce in this paper an algorithm that tackles the user's content dynamicity by modeling the CRS as a contextual bandit algorithm and by including a situation clustering algorithm to improve the precision of the CRS. Within a deliberately designed offline simulation framework, we conduct evaluations with real online event log data. The experimental results and detailed analysis reveal several important discoveries in context aware recommender system.