Stochastically robust personalized ranking for LSH recommendation retrieval

• المؤلفون الرئيسيون: LE, Dung D., LAUW, Hady W.
• التنسيق: text
• اللغة: English
• منشور في: Institutional Knowledge at Singapore Management University 2020
• الموضوعات: 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
• الوصول للمادة أونلاين: 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

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...