User behavior prediction method and system based on deep walk and ensemble learning
The invention discloses a user behavior prediction method and system based on deep walk and ensemble learning. According to the method, preprocessing work is carried out on the problems of repetition,abnormality, redundancy and the like existing in an original data set, statistical information and a...
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| creator | WU ZHILIANG CHEN ZUO ZHU SANGZHI GU HAORAN YANG SHENGGANG YANG JIELIN |
| description | The invention discloses a user behavior prediction method and system based on deep walk and ensemble learning. According to the method, preprocessing work is carried out on the problems of repetition,abnormality, redundancy and the like existing in an original data set, statistical information and activeness information capable of reflecting behavioral habits and preference degrees of consumers are extracted from the preprocessed data set to construct a user portrait for the user, then, random walk is carried out through a social network graph structure of commodities purchased by the user toobtain a new behavior sequence; and then, a Word2vec model is used to obtain the upper and lower information of each behavior of the user, and the upper and lower information is added into a machinelearning model for training and learning, so that the prediction reliability and prediction precision of the model are improved.
本发明公开了基于深度游走和集成学习的用户行为预测方法及系统，本发明对原始数据集中存在的重复、异常和冗余等问题进行了预处理工作，从预处理后的数据集中提取出能够反映消费者行为习惯和偏好程度的统计信息和 |
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本发明公开了基于深度游走和集成学习的用户行为预测方法及系统，本发明对原始数据集中存在的重复、异常和冗余等问题进行了预处理工作，从预处理后的数据集中提取出能够反映消费者行为习惯和偏好程度的统计信息和</description><language>chi ; eng</language><subject>CALCULATING ; COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS ; COMPUTING ; COUNTING ; DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FORADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORYOR FORECASTING PURPOSES ; ELECTRIC DIGITAL DATA PROCESSING ; HANDLING RECORD CARRIERS ; PHYSICS ; PRESENTATION OF DATA ; RECOGNITION OF DATA ; RECORD CARRIERS ; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE,COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTINGPURPOSES, NOT OTHERWISE PROVIDED FOR</subject><creationdate>2020</creationdate><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://worldwide.espacenet.com/publicationDetails/biblio?FT=D&date=20200922&DB=EPODOC&CC=CN&NR=111695042A$$EHTML$$P50$$Gepo$$Hfree_for_read</linktohtml><link.rule.ids>230,308,776,881,25542,76290</link.rule.ids><linktorsrc>$$Uhttps://worldwide.espacenet.com/publicationDetails/biblio?FT=D&date=20200922&DB=EPODOC&CC=CN&NR=111695042A$$EView_record_in_European_Patent_Office$$FView_record_in_$$GEuropean_Patent_Office$$Hfree_for_read</linktorsrc></links><search><creatorcontrib>WU ZHILIANG</creatorcontrib><creatorcontrib>CHEN ZUO</creatorcontrib><creatorcontrib>ZHU SANGZHI</creatorcontrib><creatorcontrib>GU HAORAN</creatorcontrib><creatorcontrib>YANG SHENGGANG</creatorcontrib><creatorcontrib>YANG JIELIN</creatorcontrib><title>User behavior prediction method and system based on deep walk and ensemble learning</title><description>The invention discloses a user behavior prediction method and system based on deep walk and ensemble learning. According to the method, preprocessing work is carried out on the problems of repetition,abnormality, redundancy and the like existing in an original data set, statistical information and activeness information capable of reflecting behavioral habits and preference degrees of consumers are extracted from the preprocessed data set to construct a user portrait for the user, then, random walk is carried out through a social network graph structure of commodities purchased by the user toobtain a new behavior sequence; and then, a Word2vec model is used to obtain the upper and lower information of each behavior of the user, and the upper and lower information is added into a machinelearning model for training and learning, so that the prediction reliability and prediction precision of the model are improved.
本发明公开了基于深度游走和集成学习的用户行为预测方法及系统，本发明对原始数据集中存在的重复、异常和冗余等问题进行了预处理工作，从预处理后的数据集中提取出能够反映消费者行为习惯和偏好程度的统计信息和</description><subject>CALCULATING</subject><subject>COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS</subject><subject>COMPUTING</subject><subject>COUNTING</subject><subject>DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FORADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORYOR FORECASTING PURPOSES</subject><subject>ELECTRIC DIGITAL DATA PROCESSING</subject><subject>HANDLING RECORD CARRIERS</subject><subject>PHYSICS</subject><subject>PRESENTATION OF DATA</subject><subject>RECOGNITION OF DATA</subject><subject>RECORD CARRIERS</subject><subject>SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE,COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTINGPURPOSES, NOT OTHERWISE PROVIDED FOR</subject><fulltext>true</fulltext><rsrctype>patent</rsrctype><creationdate>2020</creationdate><recordtype>patent</recordtype><sourceid>EVB</sourceid><recordid>eNqNyjsKAjEURuFpLETdw3UBgvEFljIoVjZqPdxM_nGCeZEbFHeviAuwOsV3htX5Ksik0fPDxkwpw9i22BjIo_TREAdD8pICT5oFhj5kgERPdvevIgi8diAHzsGG27gadOwEk19H1fSwv9THGVJsIIlbBJSmPimlNtv1fLXYLf953jybOCQ</recordid><startdate>20200922</startdate><enddate>20200922</enddate><creator>WU ZHILIANG</creator><creator>CHEN ZUO</creator><creator>ZHU SANGZHI</creator><creator>GU HAORAN</creator><creator>YANG SHENGGANG</creator><creator>YANG JIELIN</creator><scope>EVB</scope></search><sort><creationdate>20200922</creationdate><title>User behavior prediction method and system based on deep walk and ensemble learning</title><author>WU ZHILIANG ; CHEN ZUO ; ZHU SANGZHI ; GU HAORAN ; YANG SHENGGANG ; YANG JIELIN</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-epo_espacenet_CN111695042A3</frbrgroupid><rsrctype>patents</rsrctype><prefilter>patents</prefilter><language>chi ; eng</language><creationdate>2020</creationdate><topic>CALCULATING</topic><topic>COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS</topic><topic>COMPUTING</topic><topic>COUNTING</topic><topic>DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FORADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORYOR FORECASTING PURPOSES</topic><topic>ELECTRIC DIGITAL DATA PROCESSING</topic><topic>HANDLING RECORD CARRIERS</topic><topic>PHYSICS</topic><topic>PRESENTATION OF DATA</topic><topic>RECOGNITION OF DATA</topic><topic>RECORD CARRIERS</topic><topic>SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE,COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTINGPURPOSES, NOT OTHERWISE PROVIDED FOR</topic><toplevel>online_resources</toplevel><creatorcontrib>WU ZHILIANG</creatorcontrib><creatorcontrib>CHEN ZUO</creatorcontrib><creatorcontrib>ZHU SANGZHI</creatorcontrib><creatorcontrib>GU HAORAN</creatorcontrib><creatorcontrib>YANG SHENGGANG</creatorcontrib><creatorcontrib>YANG JIELIN</creatorcontrib><collection>esp@cenet</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>WU ZHILIANG</au><au>CHEN ZUO</au><au>ZHU SANGZHI</au><au>GU HAORAN</au><au>YANG SHENGGANG</au><au>YANG JIELIN</au><format>patent</format><genre>patent</genre><ristype>GEN</ristype><title>User behavior prediction method and system based on deep walk and ensemble learning</title><date>2020-09-22</date><risdate>2020</risdate><abstract>The invention discloses a user behavior prediction method and system based on deep walk and ensemble learning. According to the method, preprocessing work is carried out on the problems of repetition,abnormality, redundancy and the like existing in an original data set, statistical information and activeness information capable of reflecting behavioral habits and preference degrees of consumers are extracted from the preprocessed data set to construct a user portrait for the user, then, random walk is carried out through a social network graph structure of commodities purchased by the user toobtain a new behavior sequence; and then, a Word2vec model is used to obtain the upper and lower information of each behavior of the user, and the upper and lower information is added into a machinelearning model for training and learning, so that the prediction reliability and prediction precision of the model are improved.
本发明公开了基于深度游走和集成学习的用户行为预测方法及系统，本发明对原始数据集中存在的重复、异常和冗余等问题进行了预处理工作，从预处理后的数据集中提取出能够反映消费者行为习惯和偏好程度的统计信息和</abstract><oa>free_for_read</oa></addata></record> |
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| subjects | CALCULATING COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS COMPUTING COUNTING DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FORADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORYOR FORECASTING PURPOSES ELECTRIC DIGITAL DATA PROCESSING HANDLING RECORD CARRIERS PHYSICS PRESENTATION OF DATA RECOGNITION OF DATA RECORD CARRIERS SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE,COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTINGPURPOSES, NOT OTHERWISE PROVIDED FOR |
| title | User behavior prediction method and system based on deep walk and ensemble learning |
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