Name
Abstract | ||
|---|---|---|
In this paper, we propose a Joint Collaborative Autoencoder framework that learns both user-user and item-item correlations simultaneously, leading to a more robust model and improved top-K recommendation performance. More specifically, we show how to model these user-item correlations and demonstrate the importance of careful normalization to alleviate the influence of feedback heterogeneity. Further, we adopt a pairwise hinge-based objective function to maximize the top-K precision and recall directly for top-K recommenders. Finally, a mini-batch optimization algorithm is proposed to train the proposed model. Extensive experiments on three public datasets show the effectiveness of the proposed framework over state-of-the-art non-neural and neural alternatives.
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Year | DOI | Venue |
|---|---|---|
2019 | 10.1145/3308558.3313678 | WWW '19: The Web Conference on The World Wide Web Conference WWW 2019 |
Keywords | DocType | ISBN |
Autoencoder, hinge-based loss function, recommender systems | Conference | 978-1-4503-6674-8 |
Citations | PageRank | References |
1 | 0.35 | 0 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Fan Zhou | 1 | 101 | 23.20 |
| Ziwei Zhu | 2 | 25 | 7.81 |
| Kunpeng Zhang | 3 | 156 | 26.02 |
| Goce Trajcevski | 4 | 1732 | 141.26 |
| Zhong Ting | 5 | 46 | 11.07 |