Exploring Deep Space: Learning Personalized Ranking in a Semantic Space

Jeroen BP Vuurens, Martha Larson, Arjen P de Vries

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

26 Citations (Scopus)

Abstract

Recommender systems leverage both content and user interactions to generate recommendations that fit users' preferences. The recent surge of interest in deep learning presents new opportunities for exploiting these two sources of information. To recommend items we propose to first learn a user-independent high-dimensional semantic space in which items are positioned according to their substitutability, and then learn a user-specific transformation function to transform this space into a ranking according to the user's past preferences. An advantage of the proposed architecture is that it can be used to effectively recommend items using either content that describes the items or user-item ratings. We show that this approach significantly outperforms state-of-the-art recommender systems on the MovieLens 1M dataset.
Original languageEnglish
Title of host publicationProceedings of the 1st Workshop on Deep Learning for Recommender Systems, DLRS 2016
Place of PublicationNew York, NY
PublisherAssociation for Computing Machinery (ACM)
Pages23-28
Number of pages6
ISBN (Print)978-1-4503-4795-2
DOIs
Publication statusPublished - 2016
EventDLRS 2016: 1st Workshop on Deep Learning for Recommender Systems - Boston, MA, United States
Duration: 15 Sep 201615 Sep 2016

Conference

ConferenceDLRS 2016
Abbreviated titleDLRS 2016
CountryUnited States
CityBoston, MA
Period15/09/1615/09/16

Fingerprint

Dive into the research topics of 'Exploring Deep Space: Learning Personalized Ranking in a Semantic Space'. Together they form a unique fingerprint.

Cite this