Latent factor models and aggregation operators for collaborative filtering in reciprocal recommender systems

James Neve, Ivan Palomares

Research output: Chapter in Book/Report/Conference proceedingConference contribution

34 Citations (Scopus)

Abstract

Online dating platforms help to connect people who might potentially be a good match for each other. They have exerted a signifcant societal impact over the last decade, such that about one third of new relationships in the US are now started online, for instance. Recommender Systems are widely utilized in online platforms that connect people to people in e.g. online dating and recruitment sites. These recommender approaches are fundamentally diferent from traditional user-item approaches (such as those operating on movie and shopping sites), in that they must consider the interests of both parties jointly. Latent factor models have been notably successful in the area of user-item recommendation, however they have not been investigated within user-to-user domains as of yet. In this study, we present a novel method for reciprocal recommendation using latent factor models. We also provide a frst analysis of the use of diferent preference aggregation strategies, thereby demonstrating that the aggregation function used to combine user preference scores has a signifcant impact on the outcome of the recommender system. Our evaluation results report signifcant improvements over previous nearest-neighbour and content-based methods for reciprocal recommendation, and show that the latent factor model can be used efectively on much larger datasets than previous state-of-the-art reciprocal recommender systems.

Original languageEnglish
Title of host publicationRecSys 2019 - 13th ACM Conference on Recommender Systems
PublisherAssociation for Computing Machinery, Inc
Pages219-227
Number of pages9
ISBN (Electronic)9781450362436
DOIs
Publication statusPublished - 2019 Sept 10
Event13th ACM Conference on Recommender Systems, RecSys 2019 - Copenhagen, Denmark
Duration: 2019 Sept 162019 Sept 20

Publication series

NameRecSys 2019 - 13th ACM Conference on Recommender Systems

Conference

Conference13th ACM Conference on Recommender Systems, RecSys 2019
Country/TerritoryDenmark
CityCopenhagen
Period19-09-1619-09-20

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering
  • Software
  • Information Systems
  • Computer Science Applications

Fingerprint

Dive into the research topics of 'Latent factor models and aggregation operators for collaborative filtering in reciprocal recommender systems'. Together they form a unique fingerprint.

Cite this