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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 …
Unbiased Learning to Rank: On Recent Advances and Practical Applications
Since its inception, the field of unbiased learning to rank (ULTR) has remained very active
and has seen several impactful advancements in recent years. This tutorial provides both an …
and has seen several impactful advancements in recent years. This tutorial provides both an …
User modeling and user profiling: A comprehensive survey
The integration of artificial intelligence (AI) into daily life, particularly through information
retrieval and recommender systems, has necessitated advanced user modeling and …
retrieval and recommender systems, has necessitated advanced user modeling and …
On (Normalised) Discounted Cumulative Gain as an Off-Policy Evaluation Metric for Top-n Recommendation
Approaches to recommendation are typically evaluated in one of two ways:(1) via a
(simulated) online experiment, often seen as the gold standard, or (2) via some offline …
(simulated) online experiment, often seen as the gold standard, or (2) via some offline …
Can clicks be both labels and features? unbiased behavior feature collection and uncertainty-aware learning to rank
Using implicit feedback collected from user clicks as training labels for learning-to-rank
algorithms is a well-developed paradigm that has been extensively studied and used in …
algorithms is a well-developed paradigm that has been extensively studied and used in …
Safe deployment for counterfactual learning to rank with exposure-based risk minimization
Counterfactual learning to rank (CLTR) relies on exposure-based inverse propensity scoring
(IPS), a LTR-specific adaptation of IPS to correct for position bias. While IPS can provide …
(IPS), a LTR-specific adaptation of IPS to correct for position bias. While IPS can provide …
Unifying online and counterfactual learning to rank: A novel counterfactual estimator that effectively utilizes online interventions
Optimizing ranking systems based on user interactions is a well-studied problem. State-of-
the-art methods for optimizing ranking systems based on user interactions are divided into …
the-art methods for optimizing ranking systems based on user interactions are divided into …
Fairness of exposure in light of incomplete exposure estimation
Fairness of exposure is a commonly used notion of fairness for ranking systems. It is based
on the idea that all items or item groups should get exposure proportional to the merit of the …
on the idea that all items or item groups should get exposure proportional to the merit of the …
Unbiased Learning to Rank Meets Reality: Lessons from Baidu's Large-Scale Search Dataset
Unbiased learning-to-rank (ULTR) is a well-established framework for learning from user
clicks, which are often biased by the ranker collecting the data. While theoretically justified …
clicks, which are often biased by the ranker collecting the data. While theoretically justified …
Unbiased learning-to-rank needs unconfounded propensity estimation
The logs of the use of a search engine provide sufficient data to train a better ranker.
However, it is well known that such implicit feedback reflects biases, and in particular a …
However, it is well known that such implicit feedback reflects biases, and in particular a …