文献类型：期刊文献

英文题名：Personalized Product Recommendation Method for Analyzing User Behavior Using DeepFM

作者：Xu, Jianqiang[1];Hu, Zhujiao[2];Zou, Junzhong[1]

机构：[1]East China Univ Sci & Technol, Sch Informat Sci & Engn, Shanghai, Peoples R China;[2]Fudan Univ, Sch Microelect, Shanghai, Peoples R China

年份：2021

卷号：17

期号：2

起止页码：369

外文期刊名：JOURNAL OF INFORMATION PROCESSING SYSTEMS

收录：EI(收录号：20212010363787);WOS:【ESCI(收录号:WOS:000669918100012)】；

基金：This work is supported by the fund of Minhang District Human Resources and Social Security Bureau (No. 11C26213100798, "Wireless intelligent handheld terminal based on RFID technology" and No. 1401H122500, "RFID intelligent handheld mobile terminal and solution for food and drug traceability system") and Jiangsu University Natural Science Research Project (No. 18kjb5200001). Also this work is supported by the project fund of Shanghai Economic and Information Commission and the application of artificial intelligence in new retail.

语种：英文

外文关键词：DeepFM; Higher-Order Feature; Hit Rate Prediction; K-Means Similarity Clustering; Low-Order Features; Personalized Product Recommendation

摘要：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.