Name
Title | ||
|---|---|---|
Latent Trajectory Modeling: A Light and Efficient Way to Introduce Time in Recommender Systems |
Abstract | ||
|---|---|---|
For recommender systems, time is often an important source of information but it is also a complex dimension to apprehend. We propose here to learn item and user representations such that any timely ordered sequence of items selected by a user will be represented as a trajectory of the user in a representation space. This allows us to rank new items for this user. We then enrich the item and user representations in order to perform rating prediction using a classical matrix factorization scheme. We demonstrate the interest of our approach regarding both item ranking and rating prediction on a series of classical benchmarks. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1145/2792838.2799676 | Proceedings of the 9th ACM Conference on Recommender Systems |
Keywords | DocType | Citations |
recommender systems | Conference | 6 |
PageRank | References | Authors |
0.47 | 14 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Élie Guàrdia-Sebaoun | 1 | 9 | 1.20 |
| Vincent Guigue | 2 | 157 | 17.41 |
| Patrick Gallinari | 3 | 1856 | 187.19 |