Multi-source shared autoencoder for cross-domain recommendation
- Publisher:
- WORLD SCIENTIFIC
- Publication Type:
- Conference Proceeding
- Citation:
- Developments of Artificial Intelligence Technologies in Computation and Robotics, 2020, pp. 463-471
- Issue Date:
- 2020-10
Closed Access
| Filename | Description | Size | |||
|---|---|---|---|---|---|
| ISKE_20200115_Wenhui.pdf | Accepted version | 561.11 kB |
Copyright Clearance Process
- Recently Added
- In Progress
- Closed Access
This item is closed access and not available.
Cross-domain recommendation has been proved to be an effective solution to the data sparsity problem, which commonly exists in recommender systems. However, a challenging issue remains to be studied: how to transfer valuable knowledge from multiple source domains and balance the effect of them to the target domain under a sparse setting. To handle the issue, we develop a multi-source shared cross-domain recommender system, which aims to extract shared latent features from multiple domains to assist the recommendation task in a sparse target domain. It’s achieved through a multiple domain-shared autoencoder and an attentive module. Then we further propose an enhanced method by making it specific to each user so that it can provide personalized services. Experiments conducted on real world datasets show that the proposed methods perform well and improve the accuracy of recommendations in the target domain even though the datasets are quite sparse.
Please use this identifier to cite or link to this item: