Fast recommendation on latent collaborative relations

One important property of collaborative filtering recommender systems is that popular items are recommended disproportionately often because they provide extensive usage data and, thus, can be recommended to more users. Compared to popular products, the niches can be as economically attractive as mainstream fare for online retailers. The online retailers can stock virtually everything, and the number of available niche products exceeds the hits by several orders of magnitude. This work addresses accuracy, coverage and prediction time issues to propose a novel latent factor model called latent collaborative relations (LCR), which transforms the recommendation problem into a nearest neighbor search problem by using the proposed scoring function. We project users and items to the latent space, and calculate their similarities based on Euclidean metric. Additionally, the proposed model provides an elegant way to incorporate with locality sensitive hashing (LSH) to provide a fast recommendation while retaining recommendation accuracy and coverage. The experimental results indicate that the speedup is significant, especially when one is confronted with large-scale data sets. As for recommendation accuracy and coverage, the proposed method is competitive on three data sets.