JIPS (한국정보처리학회)
Current Result Document : 139 / 140
| 한글제목(Korean Title) |
Personalized Product Recommendation Method for Analyzing User Behavior Using DeepFM |
| 영문제목(English Title) |
Personalized Product Recommendation Method for Analyzing User Behavior Using DeepFM |
| 저자(Author) |
Jianqiang Xu
Zhujiao Hu
Junzhong Zou
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| 원문수록처(Citation) |
VOL 17 NO. 02 PP. 0369 ~ 0384 (2021. 04) |
한글내용
(Korean Abstract) |
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영문내용
(English Abstract) |
In a personalized product recommendation system, when the amount of log data is large or sparse, the accuracy of model recommendation will be greatly affected. To solve this problem, a personalized product recommendation method using deep factorization machine (DeepFM) to analyze user behavior is proposed. Firstly, the K-means clustering algorithm is used to cluster the original log data from the perspective of similarity to reduce the data dimension. Then, through the DeepFM parameter sharing strategy, the relationship between low- and high-order feature combinations is learned from log data, and the click rate prediction model is constructed. Finally, based on the predicted click-through rate, products are recommended to users in sequence and fed back. The area under the curve (AUC) and Logloss of the proposed method are 0.8834 and 0.0253, respectively, on the Criteo dataset, and 0.7836 and 0.0348 on the KDD2012 Cup dataset, respectively. Compared with other newer recommendation methods, the proposed method can achieve better recommendation effect.
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| 키워드(Keyword) |
DeepFM
Higher-Order Feature
Hit Rate Prediction
K-Means Similarity Clustering
Low-Order Features
Personalized Product Recommendation
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| 파일첨부 |
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