The essential role of time in network-based recommendation
This addresses a key limitation in recommendation systems for users by incorporating temporal dynamics, though it is incremental as it modifies an existing method.
The paper tackles the problem that network-based recommendation methods ignore temporal information, showing this time-unaware approach overestimates performance. They propose a time-aware modification that outperforms existing methods, with particularly striking improvements in fast-aging systems.
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.