Comparative analysis of collaborative filtering techniques for the multi-criteria recommender systems

Comparative analysis of collaborative filtering techniques for the multi-criteria recommender systems

연구 분야: Verification

학회: Multimedia Tools and Applications

Recommender systems are essential tools for many e-commerce services, such as Amazon, Netflix, etc. to recommend new items to users. Among various recommendation techniques, collaborative filtering has shown tremendous performance by using the rating patterns of users. Traditional collaborative filtering, matrix factorization, and deep matrix factorization are the most representative collaborative filtering techniques. However, despite their extensive utility, the selection of the method which provides better recommendation performance is still a major concern in multi-criteria recommender systems. Most recommender systems (RSs) work only on the single criterion rating, i.e., the overall rating. Single-criterion collaborative filtering (CF) generates less reliable recommendations because it suffers from correlation-based similarity problems. However, predictions based on multiple criteria have proven more accurate. This paper compares traditional collaborative filtering, matrix factorization and deep matrix factorization in recommender systems on multi-criteria datasets. We describe details of these techniques in various aspects of recommendation quality, such as how those methods handle cold-start problems, which typically happen in collaborative filtering. We performed several experiments extensively over two real-world datasets to evaluate the performance of each method in terms of qualitative and quantitative measures and observe that deep matrix factorization techniques is superior to all other techniques.