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Found 8 papers, 3 papers with code
RecRec: Algorithmic Recourse for Recommender Systems
To the best of our knowledge, this work is the first to conceptualize and empirically test a generalized framework for generating recourses for recommender systems.
Representation Online Matters: Practical End-to-End Diversification in Search and Recommender Systems
To improve representation in search results and recommendations, we introduce end-to-end diversification, ensuring that diverse content flows throughout the various stages of these systems, from retrieval to ranking.
Fairness in Ranking under Uncertainty
We call an algorithm $\phi$-fair (for $\phi \in [0, 1]$) if it has the following property for all agents $x$ and all $k$: if agent $x$ is among the top $k$ agents with respect to merit with probability at least $\rho$ (according to the posterior merit distribution), then the algorithm places the agent among the top $k$ agents in its ranking with probability at least $\phi \rho$.
Controlling Fairness and Bias in Dynamic Learning-to-Rank
Rankings are the primary interface through which many online platforms match users to items (e. g. news, products, music, video).
Policy Learning for Fairness in Ranking
Conventional Learning-to-Rank (LTR) methods optimize the utility of the rankings to the users, but they are oblivious to their impact on the ranked items.
Recommendations as Treatments: Debiasing Learning and Evaluation
Most data for evaluating and training recommender systems is subject to selection biases, either through self-selection by the users or through the actions of the recommendation system itself.
A Semantic Approach to Summarization
Sentence extraction based summarization methods has some limitations as it doesn't go into the semantics of the document.
