Xiaojie Wang
Yu Sun
ABSTRACT
Recommendation datasets are prone to selection biases due to self-selection behavior of users and item selection process of systems. This makes explicitly combating selection biases an essential problem in training recommender systems. Most previous studies assume no unbiased data available for training. We relax this assumption and assume that a small subset of training data is unbiased. Then, we propose a novel objective that utilizes the unbiased data to adaptively assign propensity weights to biased training ratings. This objective, combined with unbiased performance estimators, alleviates the effects of selection biases on the training of recommender systems. To optimize the objective, we propose an efficient algorithm that minimizes the variance of propensity estimates for better generalized recommender systems. Extensive experiments on two real-world datasets confirm the advantages of our approach in significantly reducing both the error of rating prediction and the variance of propensity estimation.
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Index Terms
- Combating Selection Biases in Recommender Systems with a Few Unbiased Ratings
Recommendations
Selection bias mitigation in recommender system using uninteresting items based on temporal visibility
Highlights- Modeling pre-use preferences and temporal rating can identify uninteresting items.
AbstractMost collaborative filtering recommendation algorithms rely too much on the user's historical rating data. However, selection bias is common in explicit feedback data, which makes the learning of user preferences face more challenges. ...
Unbiased Learning to Rank with Unbiased Propensity Estimation
SIGIR '18: The 41st International ACM SIGIR Conference on Research & Development in Information RetrievalLearning to rank with biased click data is a well-known challenge. A variety of methods has been explored to debias click data for learning to rank such as click models, result interleaving and, more recently, the unbiased learning-to-rank framework ...
Estimation of selected parameters
Modern statistical problems often involve selection of populations (or genes for example) using the observations. After selecting the populations, it is important to estimate the corresponding parameters. These quantities are called the selected ...
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