Bibliography

Amati and Rijsbergen. Probabilistic models of information retrieval based on measuring the divergence from randomness. ACM Trans. Inf. Syst. 2002. [link]
Amati. Frequentist and Bayesian Approach to Information Retrieval. ECIR 2006. [link]
Amati. Probability models for information retrieval based on divergence from randomness. 2003. [link]
Bathwal and Samdani. State-of-the-art Query2Query Similarity. 2022. [link]
Buckley and Voorhees. Retrieval System Evaluation. 2005. [link]
Buckley and Voorhees. Retrieval evaluation with incomplete information. SIGIR 2004. [link]
Carbonell and Goldstein. The Use of MMR, Diversity-Based Reranking for Reordering Documents and Producing Summaries. SIGIR 1998. [link]
Chen et al. ICIP at TREC-2020 Deep Learning Track. TREC 2020. [link]
Cormack et al. Reciprocal rank fusion outperforms condorcet and individual rank learning methods. SIGIR 2009. [link]
Craswell et al. MS MARCO: Benchmarking Ranking Models in the Large-Data Regime. SIGIR 2021. [link]
Dai and Callan. Deeper Text Understanding for IR with Contextual Neural Language Modeling. SIGIR 2019. [link]
Diaz. Condensed List Relevance Models. ICTIR 2015. [link]
Douze et al. The Faiss library. arXiv 2024. [link]
Frayling et al. Effective Adhoc Retrieval Through Traversal of a Query-Document Graph. ECIR (3) 2024. [link]
Gospodinov et al. Doc2Query-: When Less is More. ECIR (2) 2023. [link]
Guo et al. Accelerating Large-Scale Inference with Anisotropic Vector Quantization. ICML 2020. [link]
Harman. Evaluation Issues in Information Retrieval. Inf. Process. Manag. 1992. [link]
Hashemi et al. ANTIQUE: A Non-factoid Question Answering Benchmark. ECIR (2) 2020. [link]
Hiemstra. Using Language Models for Information Retrieval. 2001. [link]
Hofstätter et al. Efficiently Teaching an Effective Dense Retriever with Balanced Topic Aware Sampling. SIGIR 2021. [link]
Jaleel et al. UMass at TREC 2004: Novelty and HARD. TREC 2004. [link]
Jones. A statistical interpretation of term specificity and its application in retrieval. J. Documentation 2004. [link]
Järvelin and Kekäläinen. Cumulated gain-based evaluation of IR techniques. ACM Trans. Inf. Syst. 2002. [link]
Kantor and Voorhees. The TREC-5 Confusion Track: Comparing Retrieval Methods for Scanned Text. Inf. Retr. 2000. [link]
Kulkarni et al. Lexically-Accelerated Dense Retrieval. SIGIR 2023. [link]
Kwon et al. Efficient Memory Management for Large Language Model Serving with PagedAttention. SOSP 2023. [link]
Li et al. Pseudo Relevance Feedback with Deep Language Models and Dense Retrievers: Successes and Pitfalls. ACM Trans. Inf. Syst. 2023. [link]
Lin et al. Distilling Dense Representations for Ranking using Tightly-Coupled Teachers. arXiv 2020. [link]
Lin et al. Supporting Interoperability Between Open-Source Search Engines with the Common Index File Format. SIGIR 2020. [link]
MacAvaney and Macdonald. On Precomputation and Caching in Information Retrieval Experiments with Pipeline Architectures. arXiv 2025. [link]
MacAvaney and Tonellotto. A Reproducibility Study of PLAID. SIGIR 2024. [link]
MacAvaney et al. Adaptive Re-Ranking as an Information-Seeking Agent. CIKM Workshops 2022. [link]
MacAvaney et al. Adaptive Re-Ranking with a Corpus Graph. CIKM 2022. [link]
MacAvaney. Artifact Sharing for Information Retrieval Research. SIGIR 2025. [link]
Macdonald et al. PyTerrier: Declarative Experimentation in Python from BM25 to Dense Retrieval. CIKM 2021. [link]
Mallia et al. Faster Learned Sparse Retrieval with Block-Max Pruning. SIGIR 2024. [link]
Metzler and Croft. A Markov random field model for term dependencies. SIGIR 2005. [link]
Munyampirwa et al. Down with the Hierarchy: The 'H' in HNSW Stands for "Hubs". arXiv 2024. [link]
Ni et al. Large Dual Encoders Are Generalizable Retrievers. EMNLP 2022. [link]
Nogueira and Lin. From doc2query to docTTTTTquery. 2019. [link]
Nogueira et al. Document Expansion by Query Prediction. arXiv 2019. [link]
Ounis et al. Terrier Information Retrieval Platform. ECIR 2005. [link]
Peng et al. Incorporating term dependency in the dfr framework. SIGIR 2007. [link]
Rathee et al. Quam: Adaptive Retrieval through Query Affinity Modelling. arXiv 2024. [link]
Rijsbergen. Information Retrieval. 1979.
Robertson et al. Okapi at TREC-7: Automatic Ad Hoc, Filtering, VLC and Interactive. TREC 1998.
Santhanam et al. ColBERTv2: Effective and Efficient Retrieval via Lightweight Late Interaction. NAACL-HLT 2022. [link]
Su et al. BRIGHT: A Realistic and Challenging Benchmark for Reasoning-Intensive Retrieval. ICLR 2025. [link]
Thakur et al. BEIR: A Heterogenous Benchmark for Zero-shot Evaluation of Information Retrieval Models. arXiv 2021. [link]
Wang et al. Text Embeddings by Weakly-Supervised Contrastive Pre-training. arXiv 2022. [link]
Webber et al. A similarity measure for indefinite rankings. ACM Trans. Inf. Syst. 2010. [link]
Wolf et al. HuggingFace's Transformers: State-of-the-art Natural Language Processing. arXiv 2019. [link]
Xiao et al. RetroMAE: Pre-Training Retrieval-oriented Language Models Via Masked Auto-Encoder. EMNLP 2022. [link]
Xiong et al. Approximate Nearest Neighbor Negative Contrastive Learning for Dense Text Retrieval. ICLR 2021. [link]
Yang et al. Anserini: Enabling the Use of Lucene for Information Retrieval Research. SIGIR 2017. [link]
Zhai and Lafferty. A study of smoothing methods for language models applied to information retrieval. ACM Trans. Inf. Syst. 2004. [link]
Ziegler et al. Improving recommendation lists through topic diversification. WWW 2005. [link]