{"title":"建议满足网页浏览:增强协同过滤使用互联网浏览日志","authors":"Royi Ronen, E. Yom-Tov, G. Lavee","doi":"10.1109/ICDE.2016.7498327","DOIUrl":null,"url":null,"abstract":"Collaborative filtering (CF) recommendation systems are one of the most popular and successful methods for recommending products to people. CF systems work by finding similarities between different people according to their past purchases, and using these similarities to suggest possible items of interest. In this work we show that CF systems can be enhanced using Internet browsing data and search engine query logs, both of which represent a rich profile of individuals' interests.","PeriodicalId":6883,"journal":{"name":"2016 IEEE 32nd International Conference on Data Engineering (ICDE)","volume":"1 1","pages":"1230-1238"},"PeriodicalIF":0.0000,"publicationDate":"2016-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"16","resultStr":"{\"title\":\"Recommendations meet web browsing: enhancing collaborative filtering using internet browsing logs\",\"authors\":\"Royi Ronen, E. Yom-Tov, G. Lavee\",\"doi\":\"10.1109/ICDE.2016.7498327\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Collaborative filtering (CF) recommendation systems are one of the most popular and successful methods for recommending products to people. CF systems work by finding similarities between different people according to their past purchases, and using these similarities to suggest possible items of interest. In this work we show that CF systems can be enhanced using Internet browsing data and search engine query logs, both of which represent a rich profile of individuals' interests.\",\"PeriodicalId\":6883,\"journal\":{\"name\":\"2016 IEEE 32nd International Conference on Data Engineering (ICDE)\",\"volume\":\"1 1\",\"pages\":\"1230-1238\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2016-05-16\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"16\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2016 IEEE 32nd International Conference on Data Engineering (ICDE)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICDE.2016.7498327\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2016 IEEE 32nd International Conference on Data Engineering (ICDE)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICDE.2016.7498327","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Recommendations meet web browsing: enhancing collaborative filtering using internet browsing logs
Collaborative filtering (CF) recommendation systems are one of the most popular and successful methods for recommending products to people. CF systems work by finding similarities between different people according to their past purchases, and using these similarities to suggest possible items of interest. In this work we show that CF systems can be enhanced using Internet browsing data and search engine query logs, both of which represent a rich profile of individuals' interests.
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