  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.
