Implicit user feedback is a fundamental dataset for personalized recommendation models. Because of its inherent characteristics of sparse one-class values, it is challenging to uncover meaningful user/item representations. In this paper, we propose dual neural personalized ranking (DualNPR), which fully exploits both user- and item-side pairwise rankings in a unified manner. The key novelties of the proposed model are three-fold: (1) DualNPR discovers mutual correlation among users and items by utilizing both user- and item-side pairwise rankings, alleviating the data sparsity problem. We stress that, unlike existing models that require extra information, DualNPR naturally augments both user- and item-side pairwise rankings from a user-item interaction matrix. (2) DualNPR is built upon deep matrix factorization to capture the variability of user/item representations. In particular, it chooses raw user/item vectors as an input and learns latent user/item representations effectively. (3) DualNPR employs a dynamic negative sampling method using an exponential function, further improving the accuracy of top-N recommendation. In experimental results over three benchmark datasets, DualNPR outperforms baseline models by 21.9-86.7% in hit rate, 14.5-105.8% in normalized discounted cumulative gain, and 5.1-23.3% in the area under the ROC curve.
|Title of host publication||The Web Conference 2019 - Proceedings of the World Wide Web Conference, WWW 2019|
|Publisher||Association for Computing Machinery, Inc|
|Number of pages||11|
|Publication status||Published - 2019 May 13|
|Event||2019 World Wide Web Conference, WWW 2019 - San Francisco, United States|
Duration: 2019 May 13 → 2019 May 17
|Name||The Web Conference 2019 - Proceedings of the World Wide Web Conference, WWW 2019|
|Conference||2019 World Wide Web Conference, WWW 2019|
|Period||19/5/13 → 19/5/17|
Bibliographical noteFunding Information:
This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (NRF-2018R1A2B6009135, NRF-2018R1A5A1081213, and NRF-2018R1A5A 1060031).
All Science Journal Classification (ASJC) codes
- Computer Networks and Communications