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Evaluation methods and measures for causal learning algorithms
L Cheng, R Guo, R Moraffah, P Sheth… - IEEE Transactions …, 2022 - ieeexplore.ieee.org
The convenient access to copious multifaceted data has encouraged machine learning
researchers to reconsider correlation-based learning and embrace the opportunity of …
researchers to reconsider correlation-based learning and embrace the opportunity of …
Causal intervention for leveraging popularity bias in recommendation
Recommender system usually faces popularity bias issues: from the data perspective, items
exhibit uneven (usually long-tail) distribution on the interaction frequency; from the method …
exhibit uneven (usually long-tail) distribution on the interaction frequency; from the method …
Bias and debias in recommender system: A survey and future directions
While recent years have witnessed a rapid growth of research papers on recommender
system (RS), most of the papers focus on inventing machine learning models to better fit …
system (RS), most of the papers focus on inventing machine learning models to better fit …
AutoDebias: Learning to debias for recommendation
Recommender systems rely on user behavior data like ratings and clicks to build
personalization model. However, the collected data is observational rather than …
personalization model. However, the collected data is observational rather than …
Deconfounded recommendation for alleviating bias amplification
Recommender systems usually amplify the biases in the data. The model learned from
historical interactions with imbalanced item distribution will amplify the imbalance by over …
historical interactions with imbalanced item distribution will amplify the imbalance by over …
Controlling fairness and bias in dynamic learning-to-rank
M Morik, A Singh, J Hong, T Joachims - Proceedings of the 43rd …, 2020 - dl.acm.org
Rankings are the primary interface through which many online platforms match users to
items (eg news, products, music, video). In these two-sided markets, not only the users draw …
items (eg news, products, music, video). In these two-sided markets, not only the users draw …
Clicks can be cheating: Counterfactual recommendation for mitigating clickbait issue
Recommendation is a prevalent and critical service in information systems. To provide
personalized suggestions to users, industry players embrace machine learning, more …
personalized suggestions to users, industry players embrace machine learning, more …
Causal inference for recommendation: Foundations, methods and applications
Recommender systems are important and powerful tools for various personalized services.
Traditionally, these systems use data mining and machine learning techniques to make …
Traditionally, these systems use data mining and machine learning techniques to make …
Doubly robust joint learning for recommendation on data missing not at random
In recommender systems, usually the ratings of a user to most items are missing and a
critical problem is that the missing ratings are often missing not at random (MNAR) in reality …
critical problem is that the missing ratings are often missing not at random (MNAR) in reality …
Contrastive learning for debiased candidate generation in large-scale recommender systems
Deep candidate generation (DCG) that narrows down the collection of relevant items from
billions to hundreds via representation learning has become prevalent in industrial …
billions to hundreds via representation learning has become prevalent in industrial …