Improving Customer Behaviour Prediction with the Item2Item model in Recommender Systems

Authors

DOI:

https://doi.org/10.4108/eai.19-12-2018.156079

Keywords:

recommender systems, sequence mining, item2item

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.

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Published

19-12-2018

How to Cite

Nguyen, T. T. S. ., Do, P. M. T. ., & Nguyen, T. T. . (2018). Improving Customer Behaviour Prediction with the Item2Item model in Recommender Systems. EAI Endorsed Transactions on Industrial Networks and Intelligent Systems, 5(17), e4. https://doi.org/10.4108/eai.19-12-2018.156079

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