Faiss: A Library for Efficient Similarity Search, Jeff Johnson, Matthijs Douze, Hervé Jégou, 2017arXiv preprint arXiv:1702.08734DOI: 10.48550/arXiv.1702.08734 - Presents Faiss, a library for efficient similarity search, which includes implementations of IVF. This work offers insights into tuning IVF parameters and managing the recall-latency trade-off.
Mining of Massive Datasets, Jure Leskovec, Anand Rajaraman, and Jeffrey D. Ullman, 2020 (Cambridge University Press) - This textbook's Chapter 3 provides a clear explanation of Locality-Sensitive Hashing (LSH), its core concepts, and how its parameters affect similarity search outcomes.