Personalized diffusions for top-n recommendation
| Autor: | Georgios B. Giannakis, George Karypis, Athanasios N. Nikolakopoulos, Dimitris Berberidis |
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| Rok vydání: | 2019 |
| Předmět: |
Computer science
business.industry Process (computing) 020206 networking & telecommunications 02 engineering and technology Recommender system Machine learning computer.software_genre Random walk Teleportation Set (abstract data type) 020204 information systems Scalability 0202 electrical engineering electronic engineering information engineering Deep neural networks Artificial intelligence business computer |
| Zdroj: | RecSys |
| Popis: | 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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