Matching Users and Items for Transfer Learning in Collaborative Filtering
Date Issued
2013
Date
2013
Author(s)
Li, Chung-Yi
Abstract
This paper investigates the possibility of transferring information between homogeneous datasets of similar users and items but both user correspondence and item correspondence are unknown. More specifically, we assume there are two rating matrices that model the same kind of preferences, and there is a significant degree of overlap between the two user sets and between the two item sets. Our goal is to find out the user correspondence and item correspondence between the two rating matrices, and utilize the correspondence for exploiting the information of one matrix to improve the quality of rating prediction in the other matrix.
For finding out the correspondence, we factorize both rating matrices and exploit the latent factors to identify the users and items. The algorithm for solving the correspondence is initially based on singular value decomposition and nearest neighbor search, and then we point out the drawbacks of singular value decomposition and use another formulation to refine its result. Finally, we introduce a simple modification of regular matrix factorization model for transferring information across matrices with the obtained correspondence. In our experiment, we show that it is possible to solve the correspondence with decently high accuracy, and even with non-perfect correspondence obtained from our method, it is still possible to improve the quality of rating prediction.
Subjects
協同式過濾
轉移學習
矩陣分解
Type
thesis
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