1Department of Computer Engineering, College of Computer, Hamedan Science and Research Branch, Islamic Azad University, Hamedan, Iran
2Department of Computer Engineering, Mahshahr Branch, Islamic Azad University, Mahshahr, Iran
3Department of Computer Engineering, Hamedan Branch, Islamic Azad University, Hamedan, Iran
RecommenderSystems (RS) providepersonalizedrecommendation according touser need by analyzingbehavior of usersandgathering theirinformation. One of the algorithms used in recommender systems is user-based Collaborative Filtering (CF) method. The idea isthat ifusershavesimilar preferencesin the past, they willprobably havesimilarpreferences in the future. The important part ofcollaborativefilteringalgorithmsis allocated todetermine similarity betweenobjects. Similarities between objects are classified to user-based similarity and item-based similarity. The most popular usedsimilarity metricsin recommender systems are Pearson correlation coefficient, Spearman rank correlation, and Cosinesimilaritymeasure.Until now, little computation has been made for optimal similarity in collaborative filtering by researchers.For this reason, in thisresearch, weproposean optimal similaritymeasure via a simple linear combination of values and ratio of ratings for user-based collaborative filtering by use ofFireflyalgorithm; and we compare our experimental results with Pearson traditional similarity measure and optimal similarity measure based on genetic algorithm. Experimental results on real datasets show that proposed method not only improves recommendation accuracy significantly but also increases quality of prediction and recommendation performance.