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.
Author(s): Ryo Matsui, Suguru Yaginuma, Taketo Naito, Kazuhide Nakata
Publisher: Springer
Year: 2022
Language: English
Pages: 307–332
Noise–Robust Sampling for Collaborative Metric Learning
Abstract
1 Introduction
2 Preliminaries
2.1 Problem Formulation and Notation
2.2 Matrix Factorization (MF)
2.3 Collaborative Metric Learning (CML)
2.4 Weighted Negative Sampling
2.5 2-Stage Negative Sampling
3 Proposed Method
3.1 Estimate Latent Probability & Calculate Noise Rate
3.2 Proposed Method A: Double-Weighted Sampling
3.3 Proposed Method B: Double-Clean Sampling
4 Theoretical Analysis
4.1 Validity of the Reliability
4.2 Scalability
5 Experiments
5.1 Datasets
5.2 Baselines and Proposed Model
5.3 Metrics
5.4 Hyperparameters
5.5 Results
5.6 Explainability of Embeddings
5.7 Discussion
6 Conclusion
References
