Improving Customer Behaviour Prediction with the Item2Item model in Recommender Systems
Journal Title: EAI Endorsed Transactions on Industrial Networks and Intelligent Systems - Year 2018, Vol 5, Issue 17
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.
Authors and Affiliations
T. T. S. Nguyen, P. M. T. Do, T. T. Nguyen
Keywords
Related Articles
- EP ID EP46095
- DOI http://dx.doi.org/10.4108/eai.19-12-2018.156079
- Views 319
- Downloads 0
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