Interactive explanations of how search actually behaves. One phenomenon, one interaction, no framework.
Two constants nobody ever feels decide what BM25 thinks a document is about.
Robertson & Zaragoza, The Probabilistic Relevance Framework (2009)
Why a word's weight comes from how rare it is, and why that weight can go negative.
Spärck Jones, A Statistical Interpretation of Term Specificity (1972)
Same points, three metrics, three different nearest neighbours.
Steck, Ekanadham & Kallus, Is Cosine-Similarity Really About Similarity? (WWW 2024)
Reciprocal rank fusion throws away the scores. That is its robustness and its blind spot.
Cormack, Clarke & Buettcher, Reciprocal Rank Fusion (SIGIR 2009)
Every approximate index is a bet about where you can afford to be wrong. Draw the trade-off yourself.
Malkov & Yashunin, Efficient and robust ANN search using HNSW (2016)
Drag one document and watch five evaluation measures disagree about whether you improved anything.
Järvelin & Kekäläinen, Cumulated gain-based evaluation of IR techniques (TOIS 2002)
Why a few documents in your vector database are everyone's nearest neighbour.
Radovanović, Nanopoulos & Ivanović, Hubs in Space (JMLR 2010)
Some patterns of relevance no single vector per document can express, however good the model.
Weller, Boratko, Naim & Lee, On the Theoretical Limitations of Embedding-Based Retrieval (2025)