Research On Medicine Recommendation Algorithm Based On Collaborative Filtering

In recent years,the rapid development of E-commerce has brought many conveniences to people's life.There have variety of goods in E-commerce sites,it is difficult for people to find their favorite goods,which is called "information overload" phenomenon.Recommendation systems are effective way to solve the information overload problem.Collaborative filtering recommendation algorithm is one of the most successful applied recommendation technology in E-commerce recommender systems.Although the collaborative filtering recommendation algorithms have achieved great success in the recommendation,but there still exist some issues such as the data sparse problem,lower recommendation precision,which hinders its development.Aiming at the problems existing in collaborative filtering algorithm,two aspects are studied in this paper as follows.Aiming at the problem of lower recommendation precision of collaborative filtering,medicine recommendation algorithm based on user similarity and trust is proposed.Drugs are clustered by using density-based spatial clustering of applications with noise algorithm offline to reduce the time complexity.For the sake of computing the user similarity more precisely,co-rated drugs threshold is introduced to build similar neighbor set of target user,at the same time similarity threshold is introduced to restrict the selection of similar neighbors,which overcomes the defects of K-Nearest Neighbor algorithm.Then,the trust computing model is designed according to recommendation credibility and score reliability.The trustworthy neighbor set of target user is selected in accordance with the degree of trust between users.Finally,drugs are recommended to target user through twice neighbor selection strategy.Experimental results show that compared with the existing algorithms,the proposed algorithm has better performance in mean absolute error,precision and recall ratio.The recommendation precision has been improved.In order to solve the problem of inaccurate calculation of user similarity in collaborative filtering recommendation algorithm,medicine recommendation algorithm combining demographic attribute is proposed.Prior to drug clustering,to reduce the time complexity and improve the performance of K-means clustering algorithm,a two-stage feature selection method is used to dimension reduction of feature space.User demographic attribute similarity is introduced when calculating user rating similarity.The similarity between user rating similarity and demographic attribute similarity is weighted linearly to get user similarity.The similar neighbor set of target user is selected according to user similarity.Finally,drugs are recommended to target user.Experimental results show that(1)compared with the direct clustering,the running time of the drug cluster after the feature selection is greatly reduced;(2)compared with the existing algorithms,the proposed algorithm is more accurate calculation of user similarity.The recommendation precision has been improved.