Robust Content-Based Recommendation Distribution System with Gaussian Mixture Model

Dat Nguyen Van, Van Toan Pham, Ta Minh Thanh

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.