Three-way recommendation integrating global and local information
- Author(s): Yuan-Yuan Ma 1 ; Heng-Ru Zhang 1 ; Yuan-Yuan Xu 1 ; Fan Min 1 ; Lei Gao 1
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View affiliations
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Affiliations:
1:
School of Computer Science, Southwest Petroleum University , Chengdu 610500 , People's Republic of China
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Affiliations:
1:
School of Computer Science, Southwest Petroleum University , Chengdu 610500 , People's Republic of China
- Source:
Volume 2018, Issue 16,
November
2018,
p.
1397 – 1401
DOI: 10.1049/joe.2018.8300 , Online ISSN 2051-3305
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The matrix factorisation approach computes a low-rank approximation of the incomplete user-item rating matrix. Existing approaches suffer from under-fitting due to the use of global information for all users and items. In this study, the authors propose a three-way recommendation model that integrates global and local information. This new model has a number of main aspects. The first is rating prediction with global and local information. A clustering and two matrix factorisation algorithms are employed for this purpose. The second is the computation of recommendation thresholds based on the decision-theoretic rough set model. Misclassification and promotion costs are considered simultaneously to build the cost matrix. The last is the determination of the recommender actions based on the prediction and thresholds. Experimental results on the well-known datasets show that authors’ proposed model improves recommendation quality in terms of average cost.
Inspec keywords: recommender systems; matrix decomposition; rough set theory; decision theory; approximation theory
Other keywords: incomplete user-item rating matrix; decision-theoretic rough set model; matrix factorisation approach; three-way recommendation model; matrix factorisation algorithms; recommendation quality; cost matrix; recommender actions; local information; global information; recommendation thresholds; rating prediction; low-rank approximation
Subjects: Combinatorial mathematics; Linear algebra (numerical analysis); Interpolation and function approximation (numerical analysis); Information networks
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