Novel Weighted Interest Similarity Measurement for Recommender Systems Using Rating Timestamp.

This paper proposes a novel similarity measurement for recommender systems that uses weighted user interests and rate timestamps. Although some works were proposed previously to include the time factor in the recommendation process, these works were based on the use of the time factor with user rates. In this work, we show that using user rates could be misleading in some cases, and we propose the use of the time factor with the hidden user interest(s) instead of user rates. The user interests are weighted according to the time factor so that recent interests are given more weight than the older ones as they are more important. Experimental results proved that our proposed similarity measurement is efficient in terms of accuracy and recommendation time.