Bilal Hawashin, Ahmad Abusukhon, Ayman Mansour
International Conference on Machine Learning and Data Analysis 2015
Abstract—
This paper proposes an efficient method to
extract user interests for recommender systems. Although
item-item content similarity has been widely used in the
literature, it could not detect certain user interests. Our
solution improves the current work in two aspects. First, it
improves the current recommender systems by detecting
actual user interests. Second, it considers many types of user
interests such as single-term interest, time interval interest,
multi-interests, and dislikes. This extractor would improve
recommender systems in many aspects. Our experiments show
that our proposed method is efficient in terms of accuracy and
execution time.