Open AccessResearch of shopping recommendation system based on improved wide-depth network
Author(s) -
Shanshan Wang
Publication year - 2020
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/768/7/072072
Subject(s) - computer science , purchasing , generalization , feature (linguistics) , preference , recommender system , product (mathematics) , e commerce , set (abstract data type) , coding (social sciences) , data mining , artificial intelligence , machine learning , human–computer interaction , world wide web , mathematical analysis , linguistics , philosophy , geometry , mathematics , statistics , programming language , economics , microeconomics , operations management
With the development of e-commerce, there are more and more commodities. How to recommend the commodities that users are interested in quickly and accurately has become an important research topic in the field of e-commerce.we propose a product recommendation algorithm based on DeepFM network. Firstly, we embed the user’s purchased products, and transform the sparse feature into the low-dimensional dense feature, while the user’s personal attribute features can express the user’s purchase intention to a certain extent, and also use embedded coding to transform the features.DeepFM considers both wide and deep (i.e. low-level and high-level) at the same time to further improve the generalization ability of the model. So we use DeepFM to predict the interest of users in purchasing goods.Learning the expression of user’s interest from user’s purchase and personal preference, so as to accurately predict user’s purchase behavior.Finally, we use the real record data set purchased by online users to evaluate the effect of the model, and compare it with other models to verify the effectiveness of the model.