Collaborative Filtering with User-Item Co-Autoregressive Models
Abstract
Deep neural networks have shown promise in collaborative filtering (CF). However, existing neural approaches are either user-based or item-based, which cannot leverage all the underlying information explicitly. We propose CF-UIcA, a neural co-autoregressive model for CF tasks, which exploits the structural correlation in the domains of both users and items. The co-autoregression allows extra desired properties to be incorporated for different tasks. Furthermore, we develop an efficient stochastic learning algorithm to handle large scale datasets. We evaluate CF-UIcA on two popular benchmarks: MovieLens 1M and Netflix, and achieve state-of-the-art performance in both rating prediction and top-N recommendation tasks, which demonstrates the effectiveness of CF-UIcA.
- Publication:
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arXiv e-prints
- Pub Date:
- December 2016
- DOI:
- 10.48550/arXiv.1612.07146
- arXiv:
- arXiv:1612.07146
- Bibcode:
- 2016arXiv161207146D
- Keywords:
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- Computer Science - Machine Learning
- E-Print:
- Published in AAAI 2018