Recommender Systems with Random Walks: A Survey

TL;DR

This survey reviews the application of random walk algorithms in recommender systems, highlighting their use cases, classification, and potential advantages over traditional content-based and collaborative filtering methods.

Contribution

It provides a comprehensive classification and analysis of how random walks are utilized in recommender systems, an area less explored compared to traditional approaches.

Findings

Random walks offer a versatile approach for recommendations.

Classification of random walk-based recommender methods.

Potential advantages over traditional models.

Abstract

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.