{"title":"使用Twitter活动进行事件推荐","authors":"Axel Magnuson, V. Dialani, Deepa Mallela","doi":"10.1145/2792838.2796556","DOIUrl":null,"url":null,"abstract":"User interactions with Twitter (social network) frequently take place on mobile devices - a user base that it strongly caters to. As much of Twitter's traffic comes with geo-tagging information associated with it, it is a natural platform for geographic recommendations. This paper proposes an event recommender system for Twitter users, which identifies twitter activity co-located with previous events, and uses it to drive geographic recommendations via item-based collaborative filtering.","PeriodicalId":325637,"journal":{"name":"Proceedings of the 9th ACM Conference on Recommender Systems","volume":"46 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2015-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"12","resultStr":"{\"title\":\"Event Recommendation using Twitter Activity\",\"authors\":\"Axel Magnuson, V. Dialani, Deepa Mallela\",\"doi\":\"10.1145/2792838.2796556\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"User interactions with Twitter (social network) frequently take place on mobile devices - a user base that it strongly caters to. As much of Twitter's traffic comes with geo-tagging information associated with it, it is a natural platform for geographic recommendations. This paper proposes an event recommender system for Twitter users, which identifies twitter activity co-located with previous events, and uses it to drive geographic recommendations via item-based collaborative filtering.\",\"PeriodicalId\":325637,\"journal\":{\"name\":\"Proceedings of the 9th ACM Conference on Recommender Systems\",\"volume\":\"46 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2015-09-16\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"12\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the 9th ACM Conference on Recommender Systems\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/2792838.2796556\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 9th ACM Conference on Recommender Systems","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/2792838.2796556","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
User interactions with Twitter (social network) frequently take place on mobile devices - a user base that it strongly caters to. As much of Twitter's traffic comes with geo-tagging information associated with it, it is a natural platform for geographic recommendations. This paper proposes an event recommender system for Twitter users, which identifies twitter activity co-located with previous events, and uses it to drive geographic recommendations via item-based collaborative filtering.
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