A SURVEY ON L-INJECTION: TOWARD EFFECTIVE COLLABORATIVE FILTERING USING UNINTERESTING ITEMS

We develop a novel framework, named as l-injection, to address the sparsity problem of recommender systems. By vigilant injecting low values to a selected set of unrated user-item pairs in a user-item matrix, we demonstrate that top-N recommendation accuracies of various collaborative filtering (CF) techniques can be significantly and consistently improved. We first adopt the notion of pre-use preferences of users toward a vast amount of unrated items. Using this notion, we identify uninteresting items that have not been rated yet but are likely to receive low ratings from users, and selectively impute them as low values. As our suggested approach is method-agnostic, it can be easily applied to a variety of CF algorithms. Through extensive experiments with three real-life datasets (e.g., Movielens, Ciao, and Watcha), we prove that our solution consistently and universally enhances the accuracies of existing CF algorithms (e.g., item-based CF, SVD-based CF, and SVD++) by 2.5 to 5 times on average. Furthermore, our solution enhance the running time of those CF methods by 1.2 to 2.3 times when its setting produces the best accuracy.