Stochastically robust personalized ranking for LSH recommendation retrieval

• Main Authors: LE, Dung D., LAUW, Hady W.
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2020
• Subjects: Data points; Latent vectors; Locality sensitive hashing; Matrix factorizations; Recommendation accuracy; State of the art; Stochasticity; Top-k items; Artificial intelligence; Artificial Intelligence and Robotics; Databases and Information Systems
• Online Access: https://ink.library.smu.edu.sg/sis_research/5123; https://ink.library.smu.edu.sg/context/sis_research/article/6127/viewcontent/5889_Article_Text_9114_1_10_20200513.pdf
• Institution: Singapore Management University

Locality Sensitive Hashing (LSH) has become one of the most commonly used approximate nearest neighbor search techniques to avoid the prohibitive cost of scanning through all data points. For recommender systems, LSH achieves efficient recommendation retrieval by encoding user and item vectors into...