User Semantic Model for Dependent Attributes to Enhance Collaborative Filtering

Recommender system provides relevant items to users from huge catalogue. Collaborative filter-ing and content-based filtering are the most widely used techniques in personalized recommender systems. Collaborative filtering uses only the user-ratings data to make predictions, while content-based filtering relies on semantic information of items for recommendation. Hybrid recommenda-tion system combines the two techniques. The aim of this work is to introduce a new approach for semantically enhanced collaborative filtering. Many works have addressed this problem by proposing hybrid solutions. In this paper, we present another hybridization technique that pre-dicts users preferences for items based on their inferred preferences for semantic information of items. For this, we design a new user semantic model by using Rocchio algorithm and we apply a latent semantic analysis to reduce the dimension of data. Applying our approach to real data, the MoviesLens 1M dataset, significant improvement can be noticed compared to usage only approach, and hybrid algorithm.