Publication Type

Conference Proceeding Article

Version

publishedVersion

Publication Date

2-2020

Abstract

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.

Keywords

Data points, Latent vectors, Locality sensitive hashing, Matrix factorizations, Recommendation accuracy, State of the art, Stochasticity, Top-k items, Artificial intelligence

Discipline

Artificial Intelligence and Robotics | Databases and Information Systems

Research Areas

Data Science and Engineering

Publication

Proceedings of the 34th AAAI Conference on Artificial Intelligence 2020, New York, February 7-12

First Page

4594

Last Page

4601

ISBN

9781577358350

Identifier

10.1609/aaai.v34i04.5889

Publisher

AAAI Press

City or Country

New York

Embargo Period

5-15-2020

Copyright Owner and License

Publisher

Additional URL

https://doi.org/10.1609/aaai.v34i04.5889

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