Abstract
Collaborative Filtering (CF) is a popular approach to generate predicted rating of a target user on an item by aggregating neighbor users' ratings; these ratings are weighted by a correlation coefficient between two users. Thus, the user-user similarity computation is a significant step in CF to select proper neighborhood and exploit suitable correlation coefficients for prediction, and multiple weighting techniques have been proposed to enhance the performance. However, existing approaches compute the similarity directly based on users' rating vectors, which may lead the system to suffer from severe low-sparsity problem, and will also cause the system to be less interpretive because the rating only represents user's preference on a certain item but does not include extra feature information like attributes or genres. In this paper, we propose a method to compute the user' correlations in latent space by incorporating matrix factorization (MF) technique, and exploit the correlation coefficients in the prediction step of CF. We have evaluated the proposed approach with variant methods on MovieLens dataset to validate the effectiveness in CF.
Original language | English |
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Title of host publication | Proceedings - 2019 IEEE 35th International Conference on Data Engineering Workshops, ICDEW 2019 |
Publisher | Institute of Electrical and Electronics Engineers Inc. |
Pages | 155-160 |
Number of pages | 6 |
ISBN (Electronic) | 9781728108902 |
DOIs | |
Publication status | Published - Apr 2019 |
Externally published | Yes |
Event | 35th IEEE International Conference on Data Engineering Workshops, ICDEW 2019 - Macau, China Duration: 8 Apr 2019 → 12 Apr 2019 |
Conference
Conference | 35th IEEE International Conference on Data Engineering Workshops, ICDEW 2019 |
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Country/Territory | China |
City | Macau |
Period | 8/04/19 → 12/04/19 |
Bibliographical note
Publisher Copyright:© 2019 IEEE.
Funding
The work described in this paper has been supported by the Innovation and Technology Fund (Project No. GHP/022/17GD) from the Innovation and Technology Commission of the Government of the Hong Kong Special Administrative Region, Top-up Fund for General Research Fund (TFG-3) and Seed Fund for General Research Fund (SFG-6) of the 2018 Dean’s Research Fund to MIT Department, the Individual Research Scheme of the Dean’s Research Fund 2017-2018 (FLASS/DRF/IRS-8) and the Internal Research Grant (RG 92/2017-2018R) of The Education University of Hong Kong, and a grant from Research Grants Council of Hong Kong Special Administrative Region, China (UGC/FDS11/E03/16).
Keywords
- Collaborative Filtering
- Information Retrieval
- Matrix Factorization
- Recommender System