Open AccessResearch of Recommendation System Based on Deep Interest Network
Author(s) -
Shanshan Wang,
Yuanyuan Pan,
Xiao Yang
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1732/1/012015
Subject(s) - computer science , feature (linguistics) , data mining , recommender system , field (mathematics) , matrix decomposition , product (mathematics) , quality (philosophy) , artificial intelligence , sparse matrix , deep learning , information retrieval , factorization , algorithm , philosophy , linguistics , eigenvalues and eigenvectors , physics , mathematics , geometry , epistemology , quantum mechanics , gaussian , pure mathematics
With the development of e-commerce, it provides users with many choices. But how to quickly and accurately recommend goods to users is an important topic in this field. Matrix factorization recommendation model based on scoring data is widely used, but data sparsity affects the recommendation quality of the model. In the paper, a product recommendation algorithm based on deep interest network is proposed. First, the user’s purchased goods and the user’s search are embedded coded, and the sparse features are transformed into low-dimensional dense features.Then, the feature vectors of the purchased goods and the search text vectors are joined together to input the deep interest network as the feature to predict the user’s interest.Finally, the effectiveness of the model is validated by using record data. The validity of the model is verified by comparing with other models.