Recommender systems offer critical services in the age of mass information. A good recommender system selects a certain item for a specific user by recognizing why the user might like the item This awareness implies that the system should model the background of the items and the users .This background modeling for recommendation is tackled through the various models of collaborative filtering with auxiliary information This paper presents variational approaches for collaborative filtering to deal with auxiliary information The proposed methods encompass variational autoencoders through augmenting structures to model the auxiliary information and to model the implicit user feedback This augmentation includes the ladder network and the generative adversarial network to extract the low-dimensional representations influenced by the auxiliary information These two augmentations are the first trial in the venue of the variational autoencoders, and we demonstrate their significant improvement on the performances in the applications of the collaborative filtering.
|Title of host publication||CIKM 2017 - Proceedings of the 2017 ACM Conference on Information and Knowledge Management|
|Publisher||Association for Computing Machinery|
|Number of pages||10|
|Publication status||Published - 2017 Nov 6|
|Event||26th ACM International Conference on Information and Knowledge Management, CIKM 2017 - Singapore, Singapore|
Duration: 2017 Nov 6 → 2017 Nov 10
|Name||International Conference on Information and Knowledge Management, Proceedings|
|Other||26th ACM International Conference on Information and Knowledge Management, CIKM 2017|
|Period||17/11/6 → 17/11/10|
Bibliographical noteFunding Information:
Œe authors would like to thank the anonymous reviewers for their valuable comments and helpful suggestions on our manuscript. Œe work is supported by the Korean ICT R&D program of MSIP/IITP (R7117-17-0219, Development of Predictive Analysis Technology on Socio-Economics using Self-Evolving Agent-Based Simulation embedded with Incremental Machine Learning).
© 2017 ACM.
All Science Journal Classification (ASJC) codes
- Decision Sciences(all)
- Business, Management and Accounting(all)