CB2CF: A neural multiview content-to-collaborative filtering model for completely cold item recommendations

Oren Barkan, Noam Koenigstein, Eylon Yogev, Ori Katz

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

55 Scopus citations

Abstract

In Recommender Systems research, algorithms are often characterized as either Collaborative Filtering (CF) or Content Based (CB). CF algorithms are trained using a dataset of user preferences while CB algorithms are typically based on item profiles. These approaches harness different data sources and therefore the resulting recommended items are generally very different. This paper presents the CB2CF, a deep neural multiview model that serves as a bridge from items content into their CF representations. CB2CF is a “real-world” algorithm designed for Microsoft Store services that handle around a billion users worldwide. CB2CF is demonstrated on movies and apps recommendations, where it is shown to outperform an alternative CB model on completely cold items.

Original languageEnglish
Title of host publicationRecSys 2019 - 13th ACM Conference on Recommender Systems
Pages228-236
Number of pages9
ISBN (Electronic)9781450362436
DOIs
StatePublished - 10 Sep 2019
Externally publishedYes
Event13th ACM Conference on Recommender Systems, RecSys 2019 - Copenhagen, Denmark
Duration: 16 Sep 201920 Sep 2019

Publication series

NameRecSys 2019 - 13th ACM Conference on Recommender Systems

Conference

Conference13th ACM Conference on Recommender Systems, RecSys 2019
Country/TerritoryDenmark
CityCopenhagen
Period16/09/1920/09/19

Keywords

  • Cold item recommendations
  • Multiview Representation Learning

Fingerprint

Dive into the research topics of 'CB2CF: A neural multiview content-to-collaborative filtering model for completely cold item recommendations'. Together they form a unique fingerprint.

Cite this