Hybrid Point of Interest Recommendation Algorithm Based on Deep Learning

Hao FENG; Kun HUANG; Jing LI; Rong GAO; Donghua LIU; Chengfang SONG

doi:10.11999/JEIT180458

Volume 41 Issue 4

Mar. 2019

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Article Navigation > Journal of Electronics & Information Technology > 2019 > 41(4): 880-887

Hao FENG, Kun HUANG, Jing LI, Rong GAO, Donghua LIU, Chengfang SONG. Hybrid Point of Interest Recommendation Algorithm Based on Deep Learning[J]. Journal of Electronics & Information Technology, 2019, 41(4): 880-887. doi: 10.11999/JEIT180458

Citation:

Hao FENG, Kun HUANG, Jing LI, Rong GAO, Donghua LIU, Chengfang SONG. Hybrid Point of Interest Recommendation Algorithm Based on Deep Learning[J]. Journal of Electronics & Information Technology, 2019, 41(4): 880-887. doi: 10.11999/JEIT180458

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Hybrid Point of Interest Recommendation Algorithm Based on Deep Learning

doi: 10.11999/JEIT180458

1.
China Ship Development and Design Center, Wuhan 430064, China
2.
Computer School, Wuhan University, Wuhan 430072, China

Funds: The National Natural Science Foundation of China (41201404), The Fundamental Research Funds for the Central Universities of China (2042015gf0009)

Received Date: 2018-05-14
Rev Recd Date: 2018-11-26

Available Online: 2018-12-05

Publish Date: 2019-04-01

Abstract

Abstract

When modeling user preferences, the current researches of recommendation ignore the problem of modeling initialization and the review information accompanied with rating information for recommender models, integrating deep learning into the recommendation system becomes a hotspot of Point-Of-Interest (POI) recommendation. In this paper, a new POI recommendation model called Matrix Factorization Model integrated with Hybrid Neural Networks (MFM-HNN) is proposed. The model improves the performance of POI recommendation by fusing review text and check-in information based on Neural Network (NN). Specifically, the convolutional neural network is used to learn the feature representation of the review text and the check-in information is initialized by using the stacked denoising autoencoder. Furthermore, the extended matrix factorization model is exploited to fuse the review information feature and the initial value of the check-in information for POI recommendation. As is shown in the experimental results on real datasets, the proposed MFM-HNN achieves better recommendation performances than the other state-of-the-art POI recommendation algorithms.
- Recommendation algorithm,
- Point-Of-Interest (POI),
- Matrix factorization,
- Neural Network (NN),
- Deep learning

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References(23)

References

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