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 binary hash codes, reducing the cost of exhaustively examining all the item vectors to identify the topk items. However, conventional matrix factorization models may suffer from performance degeneration caused by randomly-drawn LSH hash functions, directly affecting the ultimate quality of the recommendations. In this paper, we propose a framework named SRPR, which factors in the stochasticity of LSH hash functions when learning realvalued user and item latent vectors, eventually improving the recommendation accuracy after LSH indexing. Experiments on publicly available datasets show that the proposed framework not only effectively learns user’s preferences for prediction, but also achieves high compatibility with LSH stochasticity, producing superior post-LSH indexing performances as compared to state-of-the-art baselines.