Hierarchical Negative Binomial Factorization for Recommender Systems on Implicit Feedback
Journal
35th AAAI Conference on Artificial Intelligence, AAAI 2021
Journal Volume
5A
Pages
4181-4188
Date Issued
2021
Author(s)
Kuo L.-Y
Abstract
When exposed to an item in a recommender system, a user may consume it (known as success exposure) or neglect it (known as failure exposure). The recently proposed methods that consider both success and failure exposure merely regard failure exposure as a constant prior, thus being capable of neither modeling various user behavior nor adapting to overdispersed data. In this paper, we propose a novel model, hierarchical negative binomial factorization, which models data dispersion via a hierarchical Bayesian structure, thus alleviating the effect of data overdispersion to help with performance gain for recommendation. Moreover, we factorize the dispersion of zero entries approximately into two low-rank matrices, thus reducing the updating time linear to the number of nonzero entries. The experiment shows that the proposed model outperforms state-of-the-art Poisson-based methods merely with a slight loss of inference speed. Copyright © 2021, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved.
Other Subjects
Artificial intelligence; Behavioral research; Dispersions; Hierarchical systems; Matrix factorization; User profile; Bayesian structure; Data dispersion; Exposed to; Hierarchical bayesian; Implicit feedback; Modeling data; Negative binomial; Overdispersion; Performance Gain; User behaviors; Recommender systems
Type
conference paper