Neural hybrid recommender: Recommendation needs collaboration

Ezgi Yıldırım*, Payam Azad, Şule Gündüz Öğüdücü

*Corresponding author for this work

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

5 Citations (Scopus)

Abstract

In recent years, deep learning has gained an indisputable success in computer vision, speech recognition, and natural language processing. After its rising success on these challenging areas, it has been studied on recommender systems as well, but mostly to include content features into traditional methods. In this paper, we introduce a generalized neural network-based recommender framework that is easily extendable by additional networks. This framework named NHR, short for Neural Hybrid Recommender allows us to include more elaborate information from the same and different data sources. We have worked on item prediction problems, but the framework can be used for rating prediction problems as well with a single change on the loss function. To evaluate the effect of such a framework, we have tested our approach on benchmark and not yet experimented datasets. The results in these real-world datasets show the superior performance of our approach in comparison with the state-of-the-art methods.

Original languageEnglish
Title of host publicationNew Frontiers in Mining Complex Patterns - 8th International Workshop, NFMCP 2019, held in Conjunction with ECML-PKDD 2019, Revised Selected Papers
EditorsMichelangelo Ceci, Corrado Loglisci, Giuseppe Manco, Elio Masciari, Zbigniew Ras
PublisherSpringer
Pages52-66
Number of pages15
ISBN (Print)9783030488604
DOIs
Publication statusPublished - 2020
Event8th International Workshop on New Frontiers in Mining Complex Patterns, NFMCP 2019, held in conjunction with the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML-PKDD 2019 - Würzburg, Germany
Duration: 16 Sept 201916 Sept 2019

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11948 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference8th International Workshop on New Frontiers in Mining Complex Patterns, NFMCP 2019, held in conjunction with the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML-PKDD 2019
Country/TerritoryGermany
CityWürzburg
Period16/09/1916/09/19

Bibliographical note

Publisher Copyright:
© Springer Nature Switzerland AG 2020.

Funding

Acknowledgements. This study is part of the research project supported by the Scientific and Technological Research Council of Turkey (TÜBİTAK) (Project No: 5170032). This work was also supported by the Research Fund of the Istanbul Technical University (Project Number: BAP-40737). We would like to thank Kariyer.Net for providing us with the online recruiting dataset used in the paper.

FundersFunder number
TÜBİTAK
Türkiye Bilimsel ve Teknolojik Araştirma Kurumu5170032
Istanbul Teknik ÜniversitesiBAP-40737

    Keywords

    • Hybrid recommenders
    • Incomplete data
    • Learning latent representation
    • Neural networks
    • Personalization
    • Recommender systems

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