Recommender Systems with Random Walks: A Survey
It addresses the need for alternative recommendation techniques, but is incremental as it reviews existing work rather than proposing new methods.
This survey explores the use of random walks in recommender systems, classifying them as a relatively unexplored approach distinct from content-based and collaborative filtering methods.
Recommender engines have become an integral component in today's e-commerce systems. From recommending books in Amazon to finding friends in social networks such as Facebook, they have become omnipresent. Generally, recommender systems can be classified into two main categories: content based and collaborative filtering based models. Both these models build relationships between users and items to provide recommendations. Content based systems achieve this task by utilizing features extracted from the context available, whereas collaborative systems use shared interests between user-item subsets. There is another relatively unexplored approach for providing recommendations that utilizes a stochastic process named random walks. This study is a survey exploring use cases of random walks in recommender systems and an attempt at classifying them.