Practically Unbiased Pairwise Loss for Recommendation With Implicit Feedback

Area of Science:

• Computer Science
• Machine Learning
• Artificial Intelligence

Background:

• Recommender systems utilize user historical data for personalized experiences.
• User behavior data collection introduces biases, violating the independent and identically distributed (i.i.d.) assumption in supervised learning.
• Existing inverse propensity score (IPS) weighting for unbiased loss in recommender systems faces practical estimation challenges.

Purpose of the Study:

• To bridge the gap between theoretical unbiasedness and practical bias in IPS-weighted ranking loss for recommender systems.
• To develop a method for training accurate propensity models and constructing practically unbiased recommendation models.
• To enhance the generalization ability of recommendation models by reducing implementation and practical bias.

Main Methods:

• Constructed a theoretical framework to derive a generalization upper bound for existing unbiased loss functions.
• Proposed treating user feedback as a noisy proxy for item exposure, assuming a specific noise rate condition.
• Developed a noise-resistant loss function for training accurate propensity models.
• Integrated precise propensity scores to build a practically unbiased recommendation model.

Main Results:

• The theoretical framework demonstrates that reducing implementation and practical bias simultaneously improves generalization.
• Training propensity models with a noise-resistant loss function yields accurate scores.
• The proposed method, weighted by precise propensity scores, results in a practically unbiased recommendation model.
• Experimental results on public datasets validate the effectiveness of the suggested approach.

Conclusions:

• The study successfully addresses the practical bias issue in IPS-weighted unbiased loss for recommender systems.
• The proposed method offers a viable solution for training more accurate and unbiased recommendation models.
• The findings have significant implications for improving user experience in various online platforms.