Addressing Interpretability and Cold-Start in Matrix Factorization for Recommender Systems
| Autor: | Reinhard Heckel, Thomas Parnell, Celestine Dünner, Michail Vlachos, V. G. Vassiliadis, Kubilay Atasu |
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| Rok vydání: | 2019 |
| Předmět: |
Speedup
business.industry Computer science Recommender system Machine learning computer.software_genre Computer Science Applications Matrix decomposition Computational Theory and Mathematics Cold start Artificial intelligence Cluster analysis Baseline (configuration management) business computer Information Systems Interpretability |
| Zdroj: | IEEE Transactions on Knowledge and Data Engineering. 31:1253-1266 |
| ISSN: | 2326-3865 1041-4347 |
| Popis: | We consider the problem of generating interpretable recommendations by identifying overlapping co-clusters of clients and products, based only on positive or implicit feedback. Our approach is applicable on very large datasets because it exhibits almost linear complexity in the input examples and the number of co-clusters. We show, both on real industrial data and on publicly available datasets, that the recommendation accuracy of our algorithm is competitive to that of state-of-the-art matrix factorization techniques. In addition, our technique has the advantage of offering recommendations that are textually and visually interpretable. Our formulation can also address cold-start problems by gracefully meshing collaborative and content-based reasoning. Finally, we present efficient Graphical Processing Unit (GPU) implementations and demonstrate a speedup of more than 270 times over our baseline CPU implementation on a cluster of 16 GPUs. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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