Semantically Enhanced Collaborative Filtering Based on RSVD

We investigate a hybrid recommendation method that is based on two-stage data processing – first dealing with content features describing items, and then handling user behavioral data. The evaluation of the proposed method is oriented on the so-called find-good-items task, rather than on the low-error-of-ratings prediction. We focus on a case of extreme collaborative data sparsity. Our method is a combination of content features preprocessing performed by means of Random Indexing (RI), a reflective retraining of preliminary reduced item vectors according to collaborative filtering data, and vector space optimization based on Singular Value Decomposition (SVD). We demonstrate that such an approach is appropriate in high data sparsity scenarios, which disqualify the use of widely-referenced collaborative filtering methods, and allows to generate more accurate recommendations than those obtained through a hybrid method based on weighted feature combination. Moreover, the proposed solution allows to improve the recommendation accuracy without increasing the computational complexity.