Information Retrieval Journal

Papers
(The median citation count of Information Retrieval Journal is 3. The table below lists those papers that are above that threshold based on CrossRef citation counts [max. 250 papers]. The publications cover those that have been published in the past four years, i.e., from 2021-08-01 to 2025-08-01.)
ArticleCitations
Guest editorial: special issue on ECIR 202129
An in-depth study on adversarial learning-to-rank24
Kernel density estimation based factored relevance model for multi-contextual point-of-interest recommendation18
Tashaphyne0.4: a new arabic light stemmer based on rhyzome modeling approach17
Recommendations for item set completion: on the semantics of item co-occurrence with data sparsity, input size, and input modalities13
FarsNewsQA: a deep learning-based question answering system for the Persian news articles12
Learning user preferences through online conversations via personalized memory transfer9
On cross-lingual retrieval with multilingual text encoders8
Applying burst-tries for error-tolerant prefix search7
Constructing and meta-evaluating state-aware evaluation metrics for interactive search systems6
Privacy-aware document retrieval with two-level inverted indexing6
CEQE to SQET: A study of contextualized embeddings for query expansion5
Multimodal video retrieval with CLIP: a user study4
An in-depth analysis of passage-level label transfer for contextual document ranking4
Efficient query processing techniques for next-page retrieval4
Reinforcement online learning to rank with unbiased reward shaping3
Measurement of clustering effectiveness for document collections3
Shallow pooling for sparse labels3
Highlighting exact matching via marking strategies for ad hoc document ranking with pretrained contextualized language models3
Exploring latent connections in graph neural networks for session-based recommendation3
Shop by image: characterizing visual search in e-commerce3
Sequence-aware news recommendations by combining intra- with inter-session user information3
Investigating better context representations for generative question answering3
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