Cross-system Recommendation: User-modelling via Social Media versus Self-Declared Preferences

Alanazi, Sultan; Goulding, James; McAuley, Derek

Abstract

© 2016 ACM. It is increasingly rare to encounter aWeb service that doesn't engage in some form of automated recommendation, with Collaborative Filtering (CF) techniques being virtually ubiquitous as the means for delivering relevant content. Yet several key issues still remain unresolved, including optimal handling of cold starts and how best to maintain user- privacy within that context. Recent work has demonstrated a potentially fruitful line of attack in the form of cross- system user modelling, which uses features generated from one domain to bootstrap recommendations in another. In this paper we evidence the effectiveness of this approach through direct real-world user feedback, deconstructing a cross-system news recommendation service where user models are generated via social media data. It is shown that even when a relatively naive vector-space approach is used, it is possible to automatically generate user-models that provide statistically superior performance than when items are explicitly filtered based on a user's self-declared preferences. Detailed qualitative analysis of why such effects occur indicate that different models are capturing widely different areas within a user's preference space, and that hybrid models represent fertile ground for future research.

Citation

Alanazi, S., Goulding, J., & McAuley, D. (2016). Cross-system Recommendation: User-modelling via Social Media versus Self-Declared Preferences. In HT '16: Proceedings of the 27th ACM Conference on Hypertext and Social Media (183-188). https://doi.org/10.1145/2914586.2914640

• Conference Name: HT '16: 27th ACM Conference on Hypertext and Social Media
• Conference Location: Halifax, Nova Scotia, Canada
• Start Date: Jul 10, 2016
• End Date: Jul 13, 2016
• Acceptance Date: Apr 1, 2016
• Online Publication Date: Jul 10, 2016
• Publication Date: Jul 10, 2016
• Deposit Date: Sep 27, 2016
• Publicly Available Date: Sep 27, 2016
• Peer Reviewed: Peer Reviewed
• Pages: 183-188
• Book Title: HT '16: Proceedings of the 27th ACM Conference on Hypertext and Social Media
• ISBN: 9781450342476
• DOI: https://doi.org/10.1145/2914586.2914640
• Public URL: https://nottingham-repository.worktribe.com/output/801644
• Publisher URL: http://dl.acm.org/citation.cfm?doid=2914586.2914640
• Additional Information: © ACM 2016. Published in Proceedings of the 27th ACM Conference on Hypertext and Social Media, pp. 183-188, doi:10.1145/2914586.2914640. ISBN 9781450342476.