Noise–Robust Sampling for Collaborative Metric Learning

Ryo Matsui (), Suguru Yaginuma (), Taketo Naito () and Kazuhide Nakata ()
Additional contact information
Ryo Matsui: Tokyo Institute of Technology
Suguru Yaginuma: MC Digital, Inc.
Taketo Naito: SMN, Corp.
Kazuhide Nakata: Tokyo Institute of Technology

The Review of Socionetwork Strategies, 2022, vol. 16, issue 2, 307-332

Abstract: Abstract Abundant research has been conducted recently on recommendation systems. A recommendation system is a subfield of information retrieval and machine learning that aims to identify the value of objects and information for everyone. For example, recommendation systems on web services learn the latent preferences of users based on their behavioral history and display content that the system considers as s user favorite. Specifically, recommendation systems for web services have two main requirements: (1) use the embedding of users and items for predictions and (2) use implicit feedback data that does not require users’ active actions when learning. Recently, a method called collaborative metric learning (CML) has been developed to satisfy the first requirement. However, this method does not address noisy label issues caused by implicit feedback data in the second requirement. This study proposes a comprehensive and effective method to deal with noise in CML. The experimental results show that the proposed method significantly improves the performance of the two requirements compared with existing methods.

Keywords: Recommendation systems; Machine learning; Collaborative filtering; Metric learning; Implicit feedback; 68T99 (search for similar items in EconPapers)
Date: 2022
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DOI: 10.1007/s12626-022-00131-x

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