Improving Recommender Systems Using Co-Appearing and Semantically Correlated User Interests

Abstract

Recommender Systems use user interests to provide more accurate recommendations according to user actual interests and behavior. This work aims at improving recommender systems by discovering hidden user interests from the existing interests. User interest expansion would contribute in improving the accuracy of recommender systems by finding more user interests using the given ones. Two methods are proposed to perform the expansion: Expanding interests using correlated interests’ extractor and Expanding interests using word embeddings. Experimental work shows that such expanding is efficient in terms of accuracy and execution time. Therefore, expanding user interests proved to be a promising step in the improvement of the recommender systems performance.