Semi-supervised Collaborative Ranking with Push at the Top

• Main Authors: Forsati, Rana, Barjasteh, Iman, Esfahanian, Abdol-Hossein
• Format: Conference Proceeding
• Language: English
• Subjects: Information systems; Information systems > Information retrieval; Information systems > Information retrieval > Retrieval tasks and goals; Information systems > Information retrieval > Retrieval tasks and goals > Document filtering; Information systems > Information retrieval > Retrieval tasks and goals > Information extraction; Information systems > Information systems applications; Information systems > Information systems applications > Data mining
• ISSN: 2473-991X
• DOI: 10.1145/3110025.3110144

Existing collaborative ranking based recommender systems tend to perform best when there is enough observed ratings for each user, and the observed data is uniformly sampled at random. However, when the observed ratings are extremely sparse (e.g. in the case of cold-start item where no rating data is available), and are not sampled uniformly at random, existing ranking methods fail to effectively leverage side information to transduct the knowledge from existing ratings to unobserved ones. We propose a semi-supervised collaborative ranking model, dubbed S2COR, to improve the quality of cold-start item recommendation. S2COR mitigates the sparsity issue by leveraging side information about both observed and missing ratings by collaboratively learning the ranking model. This enables it to deal with the case of data missing not at random, but to also effectively incorporate the available side information in transduction. We experimentally evaluated our proposed algorithm on a number of challenging real-world datasets and compared our results against state-of-the-art models for cold-start recommendation. We show significantly higher quality recommendations with our algorithm when compared to other state-of-the-art methods.