Name
Abstract | ||
|---|---|---|
This paper introduces PerDif; a novel framework for learning personalized diffusions over item-to-item graphs for top-n recommendation. PerDif learns the teleportation probabilities of a time-inhomogeneous random walk with restarts capturing a user-specific underlying item exploration process. Such an approach can lead to significant improvements in recommendation accuracy, while also providing useful information about the users in the system. Per-user fitting can be performed in parallel and very efficiently even in large-scale settings. A comprehensive set of experiments on real-world datasets demonstrate the scalability as well as the qualitative merits of the proposed framework. PerDif achieves high recommendation accuracy, outperforming state-of-the-art competing approaches---including several recently proposed methods relying on deep neural networks.
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Year | DOI | Venue |
|---|---|---|
2019 | 10.1145/3298689.3346985 | Proceedings of the 13th ACM Conference on Recommender Systems |
Keywords | Field | DocType |
item models, random walks, top-n recommendation | Information retrieval,Computer science,Artificial intelligence,Machine learning | Conference |
ISBN | Citations | PageRank |
978-1-4503-6243-6 | 0 | 0.34 |
References | Authors | |
13 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Athanasios N. Nikolakopoulos | 1 | 59 | 9.02 |
| Dimitris Berberidis | 2 | 45 | 7.47 |
| George Karypis | 3 | 15691 | 1171.82 |
| G. B. Giannakis | 4 | 11464 | 1206.47 |