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 2021-10-01 to 2025-10-01.)
ArticleCitations
Guest editorial: special issue on ECIR 202129
An in-depth study on adversarial learning-to-rank25
Kernel density estimation based factored relevance model for multi-contextual point-of-interest recommendation20
Tashaphyne0.4: a new arabic light stemmer based on rhyzome modeling approach18
FarsNewsQA: a deep learning-based question answering system for the Persian news articles14
Recommendations for item set completion: on the semantics of item co-occurrence with data sparsity, input size, and input modalities12
Learning user preferences through online conversations via personalized memory transfer10
Applying burst-tries for error-tolerant prefix search8
On cross-lingual retrieval with multilingual text encoders7
Privacy-aware document retrieval with two-level inverted indexing6
Constructing and meta-evaluating state-aware evaluation metrics for interactive search systems6
CEQE to SQET: A study of contextualized embeddings for query expansion5
Efficient query processing techniques for next-page retrieval4
Multimodal video retrieval with CLIP: a user study4
Reinforcement online learning to rank with unbiased reward shaping4
An in-depth analysis of passage-level label transfer for contextual document ranking4
Measurement of clustering effectiveness for document collections3
Shop by image: characterizing visual search in e-commerce3
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
Shallow pooling for sparse labels3
Sequence-aware news recommendations by combining intra- with inter-session user information3
Investigating better context representations for generative question answering3
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