Recommender Systems

TL;DR

This paper reviews recent advances in recommender systems, highlighting challenges, comparing algorithms, and discussing their future roles, physical aspects, and interdisciplinary significance.

Contribution

It provides a comprehensive review of recommender systems, comparing algorithms and discussing physical and interdisciplinary perspectives for future research.

Findings

Comparison of various algorithms and their effectiveness

Discussion of physical aspects influencing system behavior

Identification of key challenges and future directions

Abstract

The ongoing rapid expansion of the Internet greatly increases the necessity of effective recommender systems for filtering the abundant information. Extensive research for recommender systems is conducted by a broad range of communities including social and computer scientists, physicists, and interdisciplinary researchers. Despite substantial theoretical and practical achievements, unification and comparison of different approaches are lacking, which impedes further advances. In this article, we review recent developments in recommender systems and discuss the major challenges. We compare and evaluate available algorithms and examine their roles in the future developments. In addition to algorithms, physical aspects are described to illustrate macroscopic behavior of recommender systems. Potential impacts and future directions are discussed. We emphasize that recommendation has a great scientific depth and combines diverse research fields which makes it of interests for physicists as well as interdisciplinary researchers.