Design of Wechat Order Recommendation System Based on Improved Collaborative Filtering Algorithm
In order to solve the problem of data sparsity in order-to-order recommendation system based on classical collaborative filtering algorithm,Apriori Association rule algorithm is added and the similarity degree based on content is fused to predict the food score,fill the score matrix and reduce the sparsity of the data;Com-bined with the personalized needs of meal ordering,the recommendation standard based on the number of people is set to further filter the recommendation list.After the comparison experiment with User-CF and Item-CF,the im-proved system effectively solves the problem of data sparsity in the classical collaborative filtering algorithm,and has better recommendation effect and good generalization performance.
recommendation systemApriori association rulesrecommended number of people
万方数据
维普
