research-article

Exploring Deep Space: Learning Personalized Ranking in a Semantic Space

Authors:

Jeroen B. P. Vuurens,

Arjen P. de VriesAuthors Info & Claims

DLRS 2016: Proceedings of the 1st Workshop on Deep Learning for Recommender Systems

Pages 23 - 28

https://doi.org/10.1145/2988450.2988457

Published: 15 September 2016 Publication History

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.

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Cited By

Molaei SHavvaei AZare HJalili M(2021)Collaborative Deep Forest Learning for Recommender SystemsIEEE Access10.1109/ACCESS.2021.30548189(22053-22061)Online publication date: 2021
https://doi.org/10.1109/ACCESS.2021.3054818
Zhou WDu YDuan MUl Haq AShah F(2021)NtCF: Neural Trust-Aware Collaborative Filtering Toward Hierarchical Recommendation ServicesArabian Journal for Science and Engineering10.1007/s13369-021-05910-2Online publication date: 26-Jun-2021
https://doi.org/10.1007/s13369-021-05910-2
Sun FZhuang HXu SWang ZZheng K(2021)A Time-aware Hybrid Algorithm for Online Recommendation ServicesMobile Networks and Applications10.1007/s11036-021-01792-827:6(2328-2338)Online publication date: 27-Oct-2021
https://doi.org/10.1007/s11036-021-01792-8
Show More Cited By

Exploring Deep Space: Learning Personalized Ranking in a Semantic Space
1. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals

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Published In

cover image ACM Other conferences

DLRS 2016: Proceedings of the 1st Workshop on Deep Learning for Recommender Systems

September 2016

47 pages

ISBN:9781450347952

DOI:10.1145/2988450

Conference Chairs:
Alexandros Karatzoglou
Telefonica Research
,
Balázs Hidasi
Gravity Research & Development
,
Domonkos Tikk
Gravity Research Development
,
Oren Sar-Shalom
IBM Research
,
Haggai Roitman
IBM Research
,
Bracha Shapira
Ben-Gurion University
,
Lior Rokach
Ben-Gurion University

Copyright © 2016 ACM.

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IBMR: IBM Research

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New York, NY, United States

Publication History

Published: 15 September 2016

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DLRS 2016

DLRS 2016: Workshop on Deep Learning for Recommender Systems

September 15, 2016

MA, Boston, USA

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Overall Acceptance Rate 11 of 27 submissions, 41%

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Cited By

Molaei SHavvaei AZare HJalili M(2021)Collaborative Deep Forest Learning for Recommender SystemsIEEE Access10.1109/ACCESS.2021.30548189(22053-22061)Online publication date: 2021
https://doi.org/10.1109/ACCESS.2021.3054818
Zhou WDu YDuan MUl Haq AShah F(2021)NtCF: Neural Trust-Aware Collaborative Filtering Toward Hierarchical Recommendation ServicesArabian Journal for Science and Engineering10.1007/s13369-021-05910-2Online publication date: 26-Jun-2021
https://doi.org/10.1007/s13369-021-05910-2
Sun FZhuang HXu SWang ZZheng K(2021)A Time-aware Hybrid Algorithm for Online Recommendation ServicesMobile Networks and Applications10.1007/s11036-021-01792-827:6(2328-2338)Online publication date: 27-Oct-2021
https://doi.org/10.1007/s11036-021-01792-8
Gupta GKatarya R(2020)Research on Understanding the Effect of Deep Learning on User PreferencesArabian Journal for Science and Engineering10.1007/s13369-020-05112-2Online publication date: 26-Nov-2020
https://doi.org/10.1007/s13369-020-05112-2
Ralph DLi YWills GGreen N(2020)Recommendations from cold starts in big dataComputing10.1007/s00607-020-00792-yOnline publication date: 29-Jan-2020
https://doi.org/10.1007/s00607-020-00792-y
Sivaparvathi VLavanya Devi GRao K(2020)A Deep Learning Sentiment Primarily Based Intelligent Product Recommendation SystemICDSMLA 201910.1007/978-981-15-1420-3_188(1847-1856)Online publication date: 19-May-2020
https://doi.org/10.1007/978-981-15-1420-3_188
Sun FZhuang HZhang JWang ZZheng K(2020)Personalized Recommendation Algorithm Considering Time SensitivityCloud Computing, Smart Grid and Innovative Frontiers in Telecommunications10.1007/978-3-030-48513-9_12(154-162)Online publication date: 23-May-2020
https://doi.org/10.1007/978-3-030-48513-9_12
Schnitzer SReis DAlkhatib WRensing CSteinmetz RHung CPapadopoulos G(2019)Preselection of documents for personalized recommendations of job postings based on word embeddingsProceedings of the 34th ACM/SIGAPP Symposium on Applied Computing10.1145/3297280.3297602(1683-1686)Online publication date: 8-Apr-2019
https://dl.acm.org/doi/10.1145/3297280.3297602
Zhang SYao LSun ATay Y(2019)Deep Learning Based Recommender SystemACM Computing Surveys10.1145/328502952:1(1-38)Online publication date: 25-Feb-2019
https://dl.acm.org/doi/10.1145/3285029
Bellini VDi Noia TDi Sciascio ESchiavone A(2019)Semantics-Aware AutoencoderIEEE Access10.1109/ACCESS.2019.2953308(1-1)Online publication date: 2019
https://doi.org/10.1109/ACCESS.2019.2953308
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