Context-Aware Recommender Systems Based on Item-Grain Context Clustering

Abstract

Context-aware recommender systems (CARS), aiming to further improve recommendation accuracy and user satisfaction by taking context information into account, has become the hottest research topic in the field of recommendation. Integrating context information into recommendation frameworks is challenging, owing to the high dimensionality of context information and the sparsity of the observations, which state-of-the-art methods do not handle well. We suggest a novel approach for context-aware recommendation based on Item-grain context clustering (named IC-CARS), which first extracts context clusters for each item based on K-means method, then incorporates context clusters into Matrix Factorization model, and thus helps to overcome the often encountered problem of data sparsity, scalability and prediction quality. Experiments on two real-world datasets and the complexity analysis show that IC-CARS is scalable and outperforms several state-of-the-art methods for recommending.