Learning the Implicit Preference of Users for Effective Recommendation
Rana Forsati, Iman Barjasteh, Dennis M. Ross, Abdol‐Hossein Esfahanian · 2017
Although recommendation systems based on the latent factor models provide an appealing solution to the collaborative filtering problem, some major issues such as data sparsity and cold-start problems, still remain open. In particular, for a large portion of items that there are not sufficient purchase records, their latent factors cannot be estimated accurately. In this paper, we aim to learn and exploit the preference of users in combination with the latent factor models to mitigate these issues and to improve recommendation accuracy. To this end, we propose a novel algorithm to accurately learn the preference of users from observed ratings and available taxonomy of items. We show that predictions made based on the extracted users' preferences enable to capture the taste of users and generates more effective recommendations than pure latent factor models. To the best of our knowledge, the proposed algorithm is the first to extract and exploit the implicit preference of users in the recommendation. We conduct thorough experiments on real datasets that demonstrate the proposed model improves significantly over state-of-the-art latent factor models.