Neural hybrid recommender: Recommendation needs collaboration

Ezgi Yıldırım; Payam Azad; Şule Gündüz Öğüdücü

doi:10.1007/978-3-030-48861-1_4

Neural hybrid recommender: Recommendation needs collaboration

Ezgi Yıldırım^*, Payam Azad, Şule Gündüz Öğüdücü

^*Bu çalışma için yazışmadan sorumlu yazar

Yapay Zeka ve Veri Mühendisliği Bölümü

Istanbul Technical University

Araştırma sonucu: ???type-name??? › Konferans katkısı › bilirkişi

5 Atıf (Scopus)

Özet

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.

Orijinal dil	İngilizce
Ana bilgisayar yayını başlığı	New Frontiers in Mining Complex Patterns - 8th International Workshop, NFMCP 2019, held in Conjunction with ECML-PKDD 2019, Revised Selected Papers
Editörler	Michelangelo Ceci, Corrado Loglisci, Giuseppe Manco, Elio Masciari, Zbigniew Ras
Yayınlayan	Springer
Sayfalar	52-66
Sayfa sayısı	15
ISBN (Basılı)	9783030488604
DOI'lar	https://doi.org/10.1007/978-3-030-48861-1_4
Yayın durumu	Yayınlandı - 2020
Etkinlik	8th 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 Süre: 16 Eyl 2019 → 16 Eyl 2019

Yayın serisi

Adı	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Hacim	11948 LNAI
ISSN (Basılı)	0302-9743
ISSN (Elektronik)	1611-3349

???event.eventtypes.event.conference???

???event.eventtypes.event.conference???	8th 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
Ülke/Bölge	Germany
Şehir	Würzburg
Periyot	16/09/19 → 16/09/19

Bibliyografik not

Publisher Copyright:
© Springer Nature Switzerland AG 2020.

Finansman

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.

Finansörler	Finansör numarası
TÜBİTAK
Türkiye Bilimsel ve Teknolojik Araştirma Kurumu	5170032
Istanbul Teknik Üniversitesi	BAP-40737

Belgeye Erişim

10.1007/978-3-030-48861-1_4

Diğer Dosyalar ve Linkler

Link to publication in Scopus

Alıntı Yap

Yıldırım, E., Azad, P., & Öğüdücü, Ş. G. (2020). Neural hybrid recommender: Recommendation needs collaboration. In M. Ceci, C. Loglisci, G. Manco, E. Masciari, & Z. Ras (Eds.), New Frontiers in Mining Complex Patterns - 8th International Workshop, NFMCP 2019, held in Conjunction with ECML-PKDD 2019, Revised Selected Papers (pp. 52-66). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Hac. 11948 LNAI). Springer. https://doi.org/10.1007/978-3-030-48861-1_4

Yıldırım, Ezgi ; Azad, Payam ; Öğüdücü, Şule Gündüz. / Neural hybrid recommender : Recommendation needs collaboration. New Frontiers in Mining Complex Patterns - 8th International Workshop, NFMCP 2019, held in Conjunction with ECML-PKDD 2019, Revised Selected Papers. editör / Michelangelo Ceci ; Corrado Loglisci ; Giuseppe Manco ; Elio Masciari ; Zbigniew Ras. Springer, 2020. pp. 52-66 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{863cebb1971a4141991eb66e5b4dcb7b,

title = "Neural hybrid recommender: Recommendation needs collaboration",

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.",

keywords = "Hybrid recommenders, Incomplete data, Learning latent representation, Neural networks, Personalization, Recommender systems",

author = "Ezgi Yıldırım and Payam Azad and {\"O}ğ{\"u}d{\"u}c{\"u}, {{\c S}ule G{\"u}nd{\"u}z}",

note = "Publisher Copyright: {\textcopyright} Springer Nature Switzerland AG 2020.; 8th 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 ; Conference date: 16-09-2019 Through 16-09-2019",

year = "2020",

doi = "10.1007/978-3-030-48861-1_4",

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isbn = "9783030488604",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

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booktitle = "New Frontiers in Mining Complex Patterns - 8th International Workshop, NFMCP 2019, held in Conjunction with ECML-PKDD 2019, Revised Selected Papers",

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Yıldırım, E, Azad, P & Öğüdücü, ŞG 2020, Neural hybrid recommender: Recommendation needs collaboration. in M Ceci, C Loglisci, G Manco, E Masciari & Z Ras (eds), New Frontiers in Mining Complex Patterns - 8th International Workshop, NFMCP 2019, held in Conjunction with ECML-PKDD 2019, Revised Selected Papers. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), hac. 11948 LNAI, Springer, pp. 52-66, 8th 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, 16/09/19. https://doi.org/10.1007/978-3-030-48861-1_4

Neural hybrid recommender: Recommendation needs collaboration. / Yıldırım, Ezgi; Azad, Payam; Öğüdücü, Şule Gündüz.
New Frontiers in Mining Complex Patterns - 8th International Workshop, NFMCP 2019, held in Conjunction with ECML-PKDD 2019, Revised Selected Papers. ed. / Michelangelo Ceci; Corrado Loglisci; Giuseppe Manco; Elio Masciari; Zbigniew Ras. Springer, 2020. p. 52-66 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Hac. 11948 LNAI).

Araştırma sonucu: ???type-name??? › Konferans katkısı › bilirkişi

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AU - Yıldırım, Ezgi

AU - Azad, Payam

AU - Öğüdücü, Şule Gündüz

N1 - Publisher Copyright: © Springer Nature Switzerland AG 2020.

PY - 2020

Y1 - 2020

N2 - 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.

AB - 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.

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KW - Incomplete data

KW - Learning latent representation

KW - Neural networks

KW - Personalization

KW - Recommender systems

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AN - SCOPUS:85085739224

SN - 9783030488604

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

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A2 - Ceci, Michelangelo

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A2 - Manco, Giuseppe

A2 - Masciari, Elio

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PB - Springer

Y2 - 16 September 2019 through 16 September 2019

ER -

Yıldırım E, Azad P, Öğüdücü ŞG. Neural hybrid recommender: Recommendation needs collaboration. In Ceci M, Loglisci C, Manco G, Masciari E, Ras Z, editörler, New Frontiers in Mining Complex Patterns - 8th International Workshop, NFMCP 2019, held in Conjunction with ECML-PKDD 2019, Revised Selected Papers. Springer. 2020. p. 52-66. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-030-48861-1_4

Neural hybrid recommender: Recommendation needs collaboration

Özet

Yayın serisi

???event.eventtypes.event.conference???

Bibliyografik not

Finansman

Belgeye Erişim

Diğer Dosyalar ve Linkler

Parmak izi

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