Overview
Between matrix factorization (MF) or Random Walk with Restart (RWR), which method works better for recommender systems? Which method handles explicit or implicit feedback data better? Does additional side information help recommendation? Recommender systems play an important role in many e-commerce services such as Amazon and Netflix to recommend new items to a user. Among various recommendation strategies, collaborative filtering has shown good performance by using rating patterns of users. MF and RWR are the most representative collaborative filtering methods. However, it is still unclear which method provides better recommendation performance despite their extensive utility.
In this paper, we provide a comparative study of MF and RWR in recommender systems. We exactly formulate each correspondence of the two methods according to various tasks in recommendation. Especially, we newly devise an RWR method using global bias term which corresponds to a MF method using biases. We describe details of the two methods in various aspects of recommendation quality such as how those methods handle cold-start problem which typically happens in collaborative filtering. We extensively perform experiments over real-world datasets to evaluate the performance of each method in terms of various measures. We observe that matrix factorization performs better with explicit feedback ratings while RWR is better with implicit ones. We also observe that exploiting global popularities of items is advantageous in the performance and that side information produces positive synergy with explicit feedback but gives negative effects with implicit one.
Explicit vs. Implicit feedback
MF performs better with explicit feedback. RWR is better when used with implicit feedback.
Global bias terms
Global bias terms improve the overall quality of recommendation in both MF and RWR.
Side information
Side information produces positive synergy when used with explicit feedback ratings but gives negative impact with implicit feedbacks.
