Similarity-based context-aware recommendation

Abstract

Abstract. Context-aware recommender systems (CARS) take context into consideration when modeling user preferences. There are two gen-eral ways to integrate context with recommendation: contextual filtering and contextual modeling. Currently, the most effective context-aware recommendation algorithms are based on a contextual modeling app-roach that estimate deviations in ratings across different contexts. In this paper, we propose context similarity as an alternative contextual model-ing approach and examine different ways to represent context similarity and incorporate it into recommendation. More specifically, we show how context similarity can be integrated into the sparse linear method and matrix factorization algorithms. Our experimental results demonstrate that learning context similarity is a more effective approach to context-aware recommendation than modeling contextual rating deviations