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Robust Content-Based Recommendation Distribution System with Gaussian Mixture Model

Van, D.N. and Pham, V.T. and Thanh, T.M. (2020) Robust Content-Based Recommendation Distribution System with Gaussian Mixture Model. In: 12th International Conference on International Conference on Computational Collective Intelligence, ICCCI 2020, 30 November 2020 through 3 December 2020.

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Abstract

Recommendation systems play an very important role in boosting purchasing consumption for many manufacturers by helping consumers find the most appropriate items. Furthermore, there is quite a range of recommendation algorithms that can be efficient; however, a content-based algorithm is always the most popular, powerful, and productive method taken at the begin time of any project. In the negative aspect, somehow content-based algorithm results accuracy is still a concern that correlates to probabilistic similarity. In addition, the similarity calculation method is another crucial that affect the accuracy of content-based recommendation in probabilistic problems. Face with these problems, we propose a new content-based recommendation based on the Gaussian mixture model to improve the accuracy with more sensitive results for probabilistic recommendation problems. Our proposed method experimented in a liquor dataset including six main flavor taste, liquor main taste tags, and some other criteria. The method clusters n liquor records relied on n vectors of six dimensions into k group (k< n) before applying a formula to sort the results. Compared our proposed algorithm with two other popular models on the above dataset, the accuracy of the experimental results not only outweighs the comparison to those of two other models but also attain a very speedy response time in real-life applications. © 2020, Springer Nature Switzerland AG.

Item Type: Conference or Workshop Item (Paper)
Divisions: Faculties > Faculty of Information Technology
Identification Number: 10.1007/978-3-030-63119-2_17
Uncontrolled Keywords: Gaussian distribution; Content-based algorithm; Content-based recommendation; Distribution systems; Gaussian Mixture Model; Real-life applications; Recommendation algorithms; Similarity calculation; Artificial intelligence
Additional Information: Conference code: 252029. Language of original document: English.
URI: http://eprints.lqdtu.edu.vn/id/eprint/9119

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