Not logged in.

Contribution Details

Type Conference or Workshop Paper
Scope Discipline-based scholarship
Published in Proceedings Yes
Title Fewer Flops at the Top: Accuracy, Diversity, and Regularization in Two-Class Collaborative Filtering
Other Titles I Want to Watch Non-Popcorn Movies Sometimes: Accuracy, Diversity, and Regularization in Probabilistic Latent Factor Models
Organization Unit
Authors
  • Bibek Paudel
  • Thilo Haas
  • Abraham Bernstein
Presentation Type paper
Item Subtype Original Work
Refereed Yes
Status Published in final form
Language
  • English
Event Title 11th ACM Conference on Recommender Systems RecSys 2017
Event Type conference
Event Location Como, Italy
Event Start Date August 27 - 2017
Event End Date August 31 - 2017
Series Name RecSys
Place of Publication New York, NY, USA
Publisher ACM Press
Abstract Text In most existing recommender systems, implicit or explicit interac- tions are treated as positive links and all unknown interactions are treated as negative links. e goal is to suggest new links that will be perceived as positive by users. However, as signed social net- works and newer content services become common, it is important to distinguish between positive and negative preferences. Even in existing applications, the cost of a negative recommendation could be high when people are looking for new jobs, friends, or places to live. In this work, we develop novel probabilistic latent factor mod- els to recommend positive links and compare them with existing methods on ve di erent openly available datasets. Our models are able to produce be er ranking lists and are e ective in the task of ranking positive links at the top, with fewer negative links ( ops). Moreover, we nd that modeling signed social networks and user preferences this way has the advantage of increasing the diversity of recommendations. We also investigate the e ect of regularization on the quality of recommendations, a ma er that has not received enough a ention in the literature. We nd that regularization pa- rameter heavily a ects the quality of recommendations in terms of both accuracy and diversity.
Digital Object Identifier 10.1145/3109859.3109916
Other Identification Number merlin-id:15001
PDF File Download from ZORA
Export BibTeX
EP3 XML (ZORA)