A User Profile Based Medical Recommendation System

With the rapidly development of Internet, online medical platform has become an essential part of medicines trade. In order to help users quickly find satisfying products in a large number of commodities, the recommendation system has been proposed. The traditional recommendation algorithm usually only takes the user-item rating into consideration, which leads low accurate of prediction. In this paper, we propose a user profile based recommendation method, which uses deep learning to analyze user behavior and construct user multi-dimensional attribute features. user profile can be constructed by analyzing information of drugs. By analyzing the historical information of user’s action, including purchasing, browsing, and collecting, we can dynamically predict rating of user on drug by a trained neural network. The experimental verification on B2B medical platform shows that the accuracy of prediction is higher than other algorithms. The proposed system can not only improve user experience, but also increase the sales of the platform.

[1]  Leo Breiman,et al.  Random Forests , 2001, Machine Learning.

[2]  Michael J. Pazzani,et al.  Learning and Revising User Profiles: The Identification of Interesting Web Sites , 1997, Machine Learning.

[3]  Geoffrey E. Hinton,et al.  ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.

[4]  Gene H. Golub,et al.  Singular value decomposition and least squares solutions , 1970, Milestones in Matrix Computation.

[5]  Zheng Wang,et al.  A deep-learning based feature hybrid framework for spatiotemporal saliency detection inside videos , 2018, Neurocomputing.

[6]  H. Sebastian Seung,et al.  Algorithms for Non-negative Matrix Factorization , 2000, NIPS.

[7]  Nava Tintarev,et al.  Reading News with a Purpose: Explaining User Profiles for Self-Actualization , 2019, UMAP.

[8]  Sara Rosenthal,et al.  Age Prediction in Blogs: A Study of Style, Content, and Online Behavior in Pre- and Post-Social Media Generations , 2011, ACL.

[9]  John Riedl,et al.  Item-based collaborative filtering recommendation algorithms , 2001, WWW '01.

[10]  Robin D. Burke,et al.  Hybrid Recommender Systems: Survey and Experiments , 2002, User Modeling and User-Adapted Interaction.

[11]  Tom Fawcett,et al.  Combining Data Mining and Machine Learning for Effective User Profiling , 1996, KDD.

[12]  Lina Yao,et al.  Deep Learning Based Recommender System , 2017, ACM Comput. Surv..

[13]  Bernadetta Maleszka.  A method for knowledge integration of ontology-based user profiles in personalised document retrieval systems , 2019, Enterp. Inf. Syst..

[14]  Jason Weston,et al.  Natural Language Processing (Almost) from Scratch , 2011, J. Mach. Learn. Res..

[15]  Michael J. Pazzani,et al.  Content-Based Recommendation Systems , 2007, The Adaptive Web.

[16]  Cardona Alzate,et al.  Predicción y selección de variables con bosques aleatorios en presencia de variables correlacionadas , 2020 .

[17]  Tara N. Sainath,et al.  FUNDAMENTAL TECHNOLOGIES IN MODERN SPEECH RECOGNITION Digital Object Identifier 10.1109/MSP.2012.2205597 , 2012 .

[18]  Cristiane Neri Nobre.  Identification and characterisation of Facebook user profiles considering interaction aspects , 2019 .

[19]  Chris D. Nugent,et al.  Ontological User Profile Modeling for Context-Aware Application Personalization , 2012, UCAmI.

[20]  Martina Ziefle,et al.  Data protectors, benefit maximizers, or facts enthusiasts: Identifying user profiles for life-logging technologies , 2019, Comput. Hum. Behav..