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Fewer Flops at the Top: Accuracy, Diversity, and Regularization in Two-Class Collaborative Filtering


Paudel, Bibek; Haas, Thilo; Bernstein, Abraham (2017). Fewer Flops at the Top: Accuracy, Diversity, and Regularization in Two-Class Collaborative Filtering. In: 11th ACM Conference on Recommender Systems RecSys 2017, Como, Italy, 27 August 2017 - 31 August 2017.

Abstract

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.

Abstract

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.

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Additional indexing

Other titles:I Want to Watch Non-Popcorn Movies Sometimes: Accuracy, Diversity, and Regularization in Probabilistic Latent Factor Models
Item Type:Conference or Workshop Item (Paper), refereed, original work
Communities & Collections:03 Faculty of Economics > Department of Informatics
Dewey Decimal Classification:000 Computer science, knowledge & systems
Language:English
Event End Date:31 August 2017
Deposited On:15 Mar 2018 11:55
Last Modified:31 Jul 2018 05:50
Publisher:ACM Press
Series Name:RecSys
OA Status:Green
Publisher DOI:https://doi.org/10.1145/3109859.3109916
Other Identification Number:merlin-id:15001

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