Feature Frequency Inverse User Frequency for Dependant Attribute to Enhance Recommendations

摘要

Recommender system provides relevant items to users from huge catalogue. Collaborative filtering 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. The aim of this work is to introduce the semantic aspect of items in a collaborative filtering process in order to enhance recommendations. Many works have addressed this problem by proposing hybrid solutions. In this paper, we present another hybridization technique that predicts users preferences for items based on their inferred preferences for semantic information of items. For this, we propose a new approach to build user semantic model by using TF-IDF measure and we provide solution 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, Content only approach and hybrid algorithm.