Providing accurate recommendations to newly joined users (or potential users, so-called cold-start users) has remained a challenging yet important problem in recommender systems. To infer the preferences of such cold-start users based on their preferences observed in other domains, several cross-domain recommendation (CDR) methods have been studied. The state-of-the-art Embedding and Mapping approach for CDR (EMCDR) aims to infer the latent vectors of cold-start users by supervised mapping from the latent space of another domain. In this paper, we propose a novel CDR framework based on semi-supervised mapping, called SSCDR, which effectively learns the cross-domain relationship even in the case that only a few number of labeled data is available. To this end, it first learns the latent vectors of users and items for each domain so that their interactions are represented by the distances, then trains a cross-domain mapping function to encode such distance information by exploiting both overlapping users as labeled data and all the items as unlabeled data. In addition, SSCDR adopts an effective inference technique that predicts the latent vectors of cold-start users by aggregating their neighborhood information. Our extensive experiments on different CDR scenarios show that SSCDR outperforms the state-of-the-art methods in terms of CDR accuracy, particularly in the realistic settings that a small portion of users overlap between two domains.
|Title of host publication||CIKM 2019 - Proceedings of the 28th ACM International Conference on Information and Knowledge Management|
|Publisher||Association for Computing Machinery|
|Number of pages||10|
|Publication status||Published - 2019 Nov 3|
|Event||28th ACM International Conference on Information and Knowledge Management, CIKM 2019 - Beijing, China|
Duration: 2019 Nov 3 → 2019 Nov 7
|Name||International Conference on Information and Knowledge Management, Proceedings|
|Conference||28th ACM International Conference on Information and Knowledge Management, CIKM 2019|
|Period||19/11/3 → 19/11/7|
Bibliographical noteFunding Information:
This research was supported by the NRF grant funded by the MSIT: (No. 2016R1E1A1A01942642) and (No. 2017M3C4A7063570), the IITP grant funded by the MSIT: (No. 2018-0-00584) and (IITP-2019-2011-1-00783).
© 2019 Association for Computing Machinery.
All Science Journal Classification (ASJC) codes
- Decision Sciences(all)
- Business, Management and Accounting(all)