117 citations · 302 across the 24 of their papers we have counts for
36 papers · 1 filter
Joint Multisided Exposure Fairness for Recommendation
Haolun Wu, Bhaskar Mitra, Chen Ma +2
Prior research on exposure fairness in the context of recommender systems has focused mostly on disparities in the exposure of individual or groups of items to individual users of…
Less is Less: When Are Snippets Insufficient for Human vs Machine Relevance Estimation?
Gabriella Kazai, Bhaskar Mitra, Anlei Dong +2
Traditional information retrieval (IR) ranking models process the full text of documents. Newer models based on Transformers, however, would incur a high computational cost when pr…
Revisiting Popularity and Demographic Biases in Recommender Evaluation and Effectiveness
Nicola Neophytou, Bhaskar Mitra, Catherine Stinson
Recommendation algorithms are susceptible to popularity bias: a tendency to recommend popular items even when they fail to meet user needs. A related issue is that the recommendati…
Intra-Document Cascading: Learning to Select Passages for Neural Document Ranking
Sebastian Hofstätter, Bhaskar Mitra, Hamed Zamani +2
An emerging recipe for achieving state-of-the-art effectiveness in neural document re-ranking involves utilizing large pre-trained language models - e.g., BERT - to evaluate all in…
Not All Relevance Scores are Equal: Efficient Uncertainty and Calibration Modeling for Deep Retrieval Models
Daniel Cohen, Bhaskar Mitra, Oleg Lesota +2
In any ranking system, the retrieval model outputs a single score for a document based on its belief on how relevant it is to a given search query. While retrieval models have cont…
MS MARCO: Benchmarking Ranking Models in the Large-Data Regime
Nick Craswell, Bhaskar Mitra, Emine Yilmaz +2
Evaluation efforts such as TREC, CLEF, NTCIR and FIRE, alongside public leaderboard such as MS MARCO, are intended to encourage research and track our progress, addressing big ques…