Network-based recommendation algorithms: A review

TL;DR

This review paper discusses network-based recommendation algorithms, compares their performance on real datasets, and explores their influence on system evolution and future research challenges.

Contribution

It provides a comprehensive review and performance comparison of network-based recommendation algorithms, highlighting open research directions.

Findings

Network-based algorithms show varied performance across datasets.

Comparison reveals strengths and weaknesses of different approaches.

Discussion on influence of recommendations on system evolution.

Abstract

Recommender systems are a vital tool that helps us to overcome the information overload problem. They are being used by most e-commerce web sites and attract the interest of a broad scientific community. A recommender system uses data on users' past preferences to choose new items that might be appreciated by a given individual user. While many approaches to recommendation exist, the approach based on a network representation of the input data has gained considerable attention in the past. We review here a broad range of network-based recommendation algorithms and for the first time compare their performance on three distinct real datasets. We present recommendation topics that go beyond the mere question of which algorithm to use - such as the possible influence of recommendation on the evolution of systems that use it - and finally discuss open research directions and challenges.