Shai Fine, Yishay Mansour
Machine Learning
We study the problem of item recommendation using complex usage data. We assume that users may interact with items in various ways, each such interaction generates a usage point which may be accompanied with multiple feedback types. In addition, each user may interact with each item multiple times. We propose a generic framework that re-models the user vectors as a post-processing step that can be applied to any Matrix Factorization (MF) method. Using an evaluation on several heterogeneous real-world datasets, we demonstrate the effectiveness of the approach and demonstrate its superiority over two alternative methods.
Shai Fine, Yishay Mansour
Machine Learning
David Carmel, Haggai Roitman, et al.
ACM TIST
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SIAM Journal on Computing
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ICTIR 2019