A Hybrid Neural Network and Hidden Markov Model for Time-aware Recommender Systems

Hanxuan Chen, Zuoquan Lin

2019

Abstract

In this paper, we propose a hybrid model that combines neural network and hidden Markov model for time-aware recommender systems. We use higher-order hidden Markov model to capture the temporal information of users and items in collaborative filtering systems. Because the computation of the transition matrix of higher-order hidden Markov model is hard, we compute the transition matrix by deep neural networks. We implement the algorithms of the hybrid model for offline batch-learning and online updating respectively. Experiments on real datasets demonstrate that the hybrid model has improvement performances over the existing recommender systems.

Paper Citation

in Harvard Style

Chen H. and Lin Z. (2019). A Hybrid Neural Network and Hidden Markov Model for Time-aware Recommender Systems.In Proceedings of the 11th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART, ISBN 978-989-758-350-6, pages 204-213. DOI: 10.5220/0007380402040213

in Bibtex Style

@conference{icaart19,
author={Hanxuan Chen and Zuoquan Lin},
title={A Hybrid Neural Network and Hidden Markov Model for Time-aware Recommender Systems},
booktitle={Proceedings of the 11th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART,},
year={2019},
pages={204-213},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0007380402040213},
isbn={978-989-758-350-6},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 11th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART,
TI - A Hybrid Neural Network and Hidden Markov Model for Time-aware Recommender Systems
SN - 978-989-758-350-6
AU - Chen H.
AU - Lin Z.
PY - 2019
SP - 204
EP - 213
DO - 10.5220/0007380402040213