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Cross-domain recommender system using multi-channel embedding with attention

Cross-domain recommender system using multi-channel embedding with attention

Material type
학위논문
Personal Author
고유경, 高有京
Title Statement
Cross-domain recommender system using multi-channel embedding with attention / Yoo Kyung Koh
Publication, Distribution, etc
Seoul :   Graduate School, Korea University,   2020  
Physical Medium
iv, 26장 : 천연색삽화 ; 26 cm
기타형태 저록
Cross-Domain Recommender System Using Multi-Channel Embedding with Attention   (DCOLL211009)000000127352  
학위논문주기
학위논문(석사)-- 고려대학교 대학원: 컴퓨터·전파통신공학과, 2020. 2
학과코드
0510   6D36   1104  
General Note
지도교수: 강재우  
Bibliography, Etc. Note
참고문헌: 장 23-26
이용가능한 다른형태자료
PDF 파일로도 이용가능;   Requires PDF file reader(application/pdf)  
비통제주제어
recommender system , multi-channel , cross-domain recommendation,,
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001 000046026212
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007 ta
008 200105s2020 ulka bmAC 000c eng
040 ▼a 211009 ▼c 211009 ▼d 211009
085 0 ▼a 0510 ▼2 KDCP
090 ▼a 0510 ▼b 6D36 ▼c 1104
100 1 ▼a 고유경, ▼g 高有京
245 1 0 ▼a Cross-domain recommender system using multi-channel embedding with attention / ▼d Yoo Kyung Koh
260 ▼a Seoul : ▼b Graduate School, Korea University, ▼c 2020
300 ▼a iv, 26장 : ▼b 천연색삽화 ; ▼c 26 cm
500 ▼a 지도교수: 강재우
502 0 ▼a 학위논문(석사)-- ▼b 고려대학교 대학원: ▼c 컴퓨터·전파통신공학과, ▼d 2020. 2
504 ▼a 참고문헌: 장 23-26
530 ▼a PDF 파일로도 이용가능; ▼c Requires PDF file reader(application/pdf)
653 ▼a recommender system ▼a multi-channel ▼a cross-domain recommendation
776 0 ▼t Cross-Domain Recommender System Using Multi-Channel Embedding with Attention ▼w (DCOLL211009)000000127352
900 1 0 ▼a Koh, Yoo Kyung, ▼e
900 1 0 ▼a 강재우, ▼g 姜在雨, ▼e 지도교수
945 ▼a KLPA

Electronic Information

No. Title Service
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Cross-domain recommender system using multi-channel embedding with attention (27회 열람)
View PDF Abstract Table of Contents

Holdings Information

No. Location Call Number Accession No. Availability Due Date Make a Reservation Service
No. 1 Location Science & Engineering Library/Stacks(Thesis)/ Call Number 0510 6D36 1104 Accession No. 123063723 Availability Available Due Date Make a Reservation Service B M
No. 2 Location Science & Engineering Library/Stacks(Thesis)/ Call Number 0510 6D36 1104 Accession No. 123063724 Availability Available Due Date Make a Reservation Service B M

Contents information

Abstract

Cross-domain recommendation is one way to alleviate the inherent sparsity problem in recommender systems. Typically, leveraging source domain data helps improving recommendation performance in the target domain. Transfer learning or domain adaptation methods are well-suited for cross-domain recommendation tasks, by transferring the shared user features.
While the important user feature may vary from domain to domain, it is very challenging to model this concept, especially when there is no meta information available.
In this paper, we therefore propose a novel method of using multi-channel embedding with domain-aware attention (MCXA). MCXA allows different domains to focus on user features differently and enables transfer learning.
In addition to domain-aware features from multi-channel embedding, MCXA can capture domain-invariant user features as well with adversarial domain classification loss. The experimental result demonstrates that multi-channel approach helps to extract domain-aware features effectively and MCXA outperforms baseline methods by a remarkable margin. Furthermore, we provide a result of using the entire data from the target domain for its practical use. We also provide analysis on domain-aware attention weights in terms of domain relatedness.

Table of Contents

1 Introduction
2 Related Work
2.1 Cross-domain Recommender Systems 
2.2 Domain Adaptation
2.3 Multi-channel Approaches 
3 Method
3.1 Embedding layer: Multi-channel user embedding with attention 
3.2 Score layer: Shared MLP layers 
3.3 Prediction layer: Domain-specific learning and domain classification
3.4 Joint learning
4 Experiments
4.1 Dataset 
4.2 Metrics
4.3 Baselines
4.4 Experimental Setting
5 Results
5.1 Performance of different cross-domain methods (RQ1)
5.2 Effectiveness of multi-channel (RQ2)
5.3 Analysis on attention weights (RQ3)
6 Conclusion

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