An Intelligent and Personalized Tobacco Brand Recommendation Method

This paper aims to solve the intelligent and personalized tobacco brand recommendation problem, which greatly affects the sales performance of tobacco enterprises. Firstly, we discuss how to mine the internal correlations between different users to compute user similarity. Particularly, we estimate user similarity by constructing user feature vectors using Cosine distance. Secondly, a novel intelligent and personalized tobacco brand recommendation algorithm is given, and the top ranked tobacco brands are output as the tobacco brand recommendation results. Finally, experiments test the effectiveness of the proposed algorithm by two main aspects, and positive results are achieved.