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inis 18(17): e4

Research Article

Improving Customer Behaviour Prediction with the Item2Item model in Recommender Systems

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  • @ARTICLE{10.4108/eai.19-12-2018.156079,
        author={T. T. S. Nguyen and P. M. T. Do and T. T. Nguyen},
        title={Improving Customer Behaviour Prediction with the Item2Item model in Recommender Systems},
        journal={EAI Endorsed Transactions on Industrial Networks and Intelligent Systems},
        volume={5},
        number={17},
        publisher={EAI},
        journal_a={INIS},
        year={2018},
        month={12},
        keywords={recommender systems, sequence mining, item2item},
        doi={10.4108/eai.19-12-2018.156079}
    }
    
  • T. T. S. Nguyen
    P. M. T. Do
    T. T. Nguyen
    Year: 2018
    Improving Customer Behaviour Prediction with the Item2Item model in Recommender Systems
    INIS
    EAI
    DOI: 10.4108/eai.19-12-2018.156079
T. T. S. Nguyen1,*, P. M. T. Do1, T. T. Nguyen2
  • 1: School of Computer Science & Engineering, International University, VNU-HCMC, Ho Chi Minh City, Vietnam
  • 2: University of Buckingham, UK
*Contact email: nttsang@hcmiu.edu.vn

Abstract

Recommender Systems are the most well-known applications in E-commerce sites. However, the trade-off between runtime and the accuracy in making recommendations is a big challenge. This work combines several traditional techniques to reduce the limitation of each single technique and exploits the Item2Item model to improve the prediction accuracy. As a case study, this paper focuses on user behaviour prediction in restaurant recommender systems and uses a public dataset including restaurant information and user sessions. Within this dataset, user behaviour can be discovered for the collaborative filtering, and restaurant information is extracted for the content-based filtering. The idea of the pre-trained word embedding in Natural Language Processing is utilized in the item-based collaborative filtering to find the similarity between restaurants based on user sessions. Experimental results have shown that the combination of these techniques makes valuable recommendations.

Keywords
recommender systems, sequence mining, item2item
Received
2018-08-31
Accepted
2018-12-12
Published
2018-12-19
Publisher
EAI
http://dx.doi.org/10.4108/eai.19-12-2018.156079

Copyright © 2018 T.T.S Nguyen et al., licensed to EAI. This is an open access article distributed under the terms of the Creative Commons Attribution licence (http://creativecommons.org/licenses/by/3.0/), which permits unlimited use, distribution and reproduction in any medium so long as the original work is properly cited.

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