Recommendations with Sparsity Based Weighted Context Framework

Context-Aware Recommender Systems (CARS) is a sort of information filtering tool which has become crucial for services in this big era of data. Owing to its characteristic of including contextual information, it achieves better results in terms of prediction accuracy. The collaborative filtering has been proved as an efficient technique to recommend items among all existing techniques in this area. Moreover, incorporation of other evolutionary techniques in it for contextualization and to alleviate sparsity problem can give an additive advantage. In this paper, we propose to find the vector of weights using particle swarm optimization to control the contribution of each context feature. It is aimed to make a balance between data sparsity and maximization of contextual effects. Further, the weighting vector is used in different components of user and item neighborhood-based algorithms. Moreover, we present a novel method to find aggregated similarity from local and global similarity based on sparsity measure. Local similarity gives importance to co-rated items while global similarity utilizes all the ratings assigned by a pair of users. The proposed algorithms are evaluated for Individual and Group Recommendations. The experimental results on two contextually rich datasets prove that the proposed algorithms outperform the other techniques of this domain. The sparsity measure that is best suited to find aggregation is dataset dependent. Finally, the algorithms show their efficacy for Group Recommendations too.