Context-Aware Recommendation Systems Using Consensus-Clustering

Abstract

Recommendation Systems (RSs) have proved a compelling performance to overcome the data overload problem. Context-aware recommenders guide users/clients to more personalized recommendations. Incorporating contextual features in recommendation systems improves the systems’ accuracy; however, they still suffer from sparsity and scalability problems which impact the quality of recommendations. In this paper, to overcome these limitations, we propose a context-aware recommendation system using the notion of consensus clustering, named CARS-CC. The proposed recommendation system is experimentally evaluated using contextual Pre-filtering and Post-filtering approaches. Experimental results show that the concept of consensus learning using clustering analysis can significantly improve the recommender systems’ accuracy. The proposed method surpasses the other recommendation algorithms in terms of accuracy, precision and recall, particularly using the Hybrid Bipartite Graph Formulation (HBGF) method. In addition, CARS-CC(hgpa) has outperformed all other clustering techniques in terms of MAE and RMSE with 23.73% and 7.54%, respectively. The MAE and RMSE results show that consensus clustering leads to better accuracy measures and a more stable resilient recommendation system. The response time taken to generate recommendations using post-filtering is less than that of the pre-filtering approach. The CARS-CC(HGPA) in the post-filtering approach; generates recommendations 58.4% faster than pre-filtering, which speeds up the recommendation process and facilitates real-time response.