The essential role of time in network-based recommendation

TL;DR

This paper emphasizes the importance of incorporating temporal information into network-based recommendation algorithms, demonstrating that time-aware methods outperform traditional structural approaches, especially in rapidly aging systems.

Contribution

It introduces a novel time-aware modification to existing recommendation methods, highlighting the significance of temporal data in improving recommendation accuracy.

Findings

Time-aware methods outperform time-unaware ones.

Performance gains are significant in systems with fast aging.

Combining temporal and structural information enhances recommendations.

Abstract

Random walks on bipartite networks have been used extensively to design personalized recommendation methods. While aging has been identified as a key component in the growth of information networks, most research has focused on the networks' structural properties and neglected the often available time information. Time has been largely ignored both by the investigated recommendation methods as well as by the methodology used to evaluate them. We show that this time-unaware approach overestimates the methods' recommendation performance. Motivated by microscopic rules of network growth, we propose a time-aware modification of an existing recommendation method and show that by combining the temporal and structural aspects, it outperforms the existing methods. The performance improvements are particularly striking in systems with fast aging.