摘要
针对协同推荐和序列表征方法在预测用户行为任务上面临的行为不确定性和数据稀疏问题,提出基于意图识别的不确定性行为序列预测(G2IE)方法. G2IE方法根据计划行为理论(TPB),对用户行为序列中受控行为模式进行挖掘;基于信息熵计算相邻受控行为之间的不确定性行为列表的行为转移意图强度;融合行为转移意图增强行为关系,弥补行为意图缺失. G2IE方法挖掘行为的不确定性关系,并用模型进行量化,用于解决行为不确定性难点;通过融合转移意图方法能够发现更多的行为关系,也在一定程度上缓解数据稀疏的问题.较其他使用行为直接关系的方法,G2IE方法有更准确丰富的表示能力.在3个公开行为数据集上进行对比实验,结果表明,本研究方法在综合指标F1值上均为最优,证明了所提方法的有效性.
An graph based intent identification embedding(G2 IE) method was proposed, in order to solve the problems of behavior uncertainty and data sparsity faced by collaborative recommendation and sequence representation methods in user behavior prediction. In G2 IE method, firstly the theory of planned behavior(TPB) is used to mine the controlled behavior patterns in the user behavior sequence, then the transfer intention intensity of the uncertain behavior list between adjacent controlled behaviors is calculated based on information entropy, and finally the behavior relationship is strengthened by integrating the behavior transfer intention to make up for the lack of behavior intention. In G2 IE method, the uncertainty of behavior is identified and it is measured with a model, in order to solve the problem of behavior randomness. The problem of data sparsity can be alleviated to some extent by discovering more behavior relationships through the fusion of transfer intention. G2 IE method has more accurate and rich expression ability compared with other methods that use behavior direct relation. Experimental results on three public user behavior datasets demonstrate the effectiveness of the proposed method.
作者
何飞
金苍宏
吴明晖
HE Fei;JIN Cang-hong;WU Ming-hui(School of Computer and Computing Science,Zhejiang University City College,Hangzhou 310015,China;College of Computer Science and Technology,Zhejiang University,Hangzhou 310027,China)
出处
《浙江大学学报（工学版）》
EI
CAS
CSCD
北大核心
2022年第2期254-262,共9页
Journal of Zhejiang University：Engineering Science
基金
浙江省自然科学基金资助项目(LY21F020003)
浙江省重点研发计划资助项目(2021C01164)
国家重点研发计划资助项目(2018YFB2101202)
国网浙江省电力有限公司科技项目(5211XT20008Q)。
关键词
行为模式挖掘
不确定性关系
意图识别
图嵌入
行为序列预测
behavior pattern mining
uncertainty relationship
intent identification
graph embedding
behavior sequence prediction
作者简介
何飞(1996—),男,硕士生,从事用户行为预测、机器学习研究.orcid.org/0000-0003-4465-9205.E-mail:fei.he@zju.edu.cn;通信联系人:吴明晖,男,教授.orcid.org/0000-0001-8179-7119.E-mail:mhwu@zucc.edu.cn。
