{"title":"社交媒体驱动的新闻个性化","authors":"S. O’Banion, L. Birnbaum, K. Hammond","doi":"10.1145/2365934.2365943","DOIUrl":null,"url":null,"abstract":"While social media have achieved significant and widespread adoption as platforms for sharing information, their use as a source of data for predicting user interests has not yet been fully explored. In this paper, we present a content-based approach to modeling user interests based on Twitter. Our recommendation system uses information retrieval techniques to represent tweets and users as collections of news topics, including high-level categories (e.g., sports, politics, business) and detailed subtopics (e.g., Chicago Bulls, Mitt Romney, entrepreneurship). We discuss the design of a system that uses this information to deliver news recommendations in the form of a personalized newspaper. Finally, we describe a novel method for evaluating recommendation systems based on Twitter that involves mining Twitter data to identify explicit indicators of news interests and comparing these to retroactive system recommendations.","PeriodicalId":258534,"journal":{"name":"Proceedings of the 4th ACM RecSys workshop on Recommender systems and the social web","volume":"8 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2012-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"22","resultStr":"{\"title\":\"Social media-driven news personalization\",\"authors\":\"S. O’Banion, L. Birnbaum, K. Hammond\",\"doi\":\"10.1145/2365934.2365943\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"While social media have achieved significant and widespread adoption as platforms for sharing information, their use as a source of data for predicting user interests has not yet been fully explored. In this paper, we present a content-based approach to modeling user interests based on Twitter. Our recommendation system uses information retrieval techniques to represent tweets and users as collections of news topics, including high-level categories (e.g., sports, politics, business) and detailed subtopics (e.g., Chicago Bulls, Mitt Romney, entrepreneurship). We discuss the design of a system that uses this information to deliver news recommendations in the form of a personalized newspaper. Finally, we describe a novel method for evaluating recommendation systems based on Twitter that involves mining Twitter data to identify explicit indicators of news interests and comparing these to retroactive system recommendations.\",\"PeriodicalId\":258534,\"journal\":{\"name\":\"Proceedings of the 4th ACM RecSys workshop on Recommender systems and the social web\",\"volume\":\"8 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2012-09-09\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"22\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the 4th ACM RecSys workshop on Recommender systems and the social web\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/2365934.2365943\",\"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 4th ACM RecSys workshop on Recommender systems and the social web","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/2365934.2365943","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
While social media have achieved significant and widespread adoption as platforms for sharing information, their use as a source of data for predicting user interests has not yet been fully explored. In this paper, we present a content-based approach to modeling user interests based on Twitter. Our recommendation system uses information retrieval techniques to represent tweets and users as collections of news topics, including high-level categories (e.g., sports, politics, business) and detailed subtopics (e.g., Chicago Bulls, Mitt Romney, entrepreneurship). We discuss the design of a system that uses this information to deliver news recommendations in the form of a personalized newspaper. Finally, we describe a novel method for evaluating recommendation systems based on Twitter that involves mining Twitter data to identify explicit indicators of news interests and comparing these to retroactive system recommendations.