Proceedings of the 4th ACM RecSys workshop on Recommender systems and the social web最新文献

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Extending FolkRank with content data 用内容数据扩展FolkRank
Nikolas Landia, S. Anand, A. Hotho, R. Jäschke, Stephan Doerfel, Folke Mitzlaff
{"title":"Extending FolkRank with content data","authors":"Nikolas Landia, S. Anand, A. Hotho, R. Jäschke, Stephan Doerfel, Folke Mitzlaff","doi":"10.1145/2365934.2365936","DOIUrl":"https://doi.org/10.1145/2365934.2365936","url":null,"abstract":"Real-world tagging datasets have a large proportion of new/ untagged documents. Few approaches for recommending tags to a user for a document address this new item problem, concentrating instead on artificially created post-core datasets where it is guaranteed that the user as well as the document of each test post is known to the system and already has some tags assigned to it. In order to recommend tags for new documents, approaches are required which model documents not only based on the tags assigned to them in the past (if any), but also the content. In this paper we present a novel adaptation to the widely recognised FolkRank tag recommendation algorithm by including content data. We adapt the FolkRank graph to use word nodes instead of document nodes, enabling it to recommend tags for new documents based on their textual content. Our adaptations make FolkRank applicable to post-core 1 ie. the full real-world tagging datasets and address the new item problem in tag recommendation. For comparison, we also apply and evaluate the same methodology of including content on a simpler tag recommendation algorithm. This results in a less expensive recommender which suggests a combination of user related and document content related tags. Including content data into FolkRank shows an improvement over plain FolkRank on full tagging datasets. However, we also observe that our simpler content-aware tag recommender outperforms FolkRank with content data. Our results suggest that an optimisation of the weighting method of FolkRank is required to achieve better results.","PeriodicalId":258534,"journal":{"name":"Proceedings of the 4th ACM RecSys workshop on Recommender systems and the social web","volume":"170 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126782813","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 10
Aggregating content and network information to curate twitter user lists 聚合内容和网络信息来管理twitter用户列表
Derek Greene, Gavin Sheridan, Barry Smyth, P. Cunningham
{"title":"Aggregating content and network information to curate twitter user lists","authors":"Derek Greene, Gavin Sheridan, Barry Smyth, P. Cunningham","doi":"10.1145/2365934.2365941","DOIUrl":"https://doi.org/10.1145/2365934.2365941","url":null,"abstract":"Twitter introduced user lists in late 2009, allowing users to be grouped according to meaningful topics or themes. Lists have since been adopted by media outlets as a means of organising content around news stories. Thus the curation of these lists is important - they should contain the key information gatekeepers and present a balanced perspective on a story. Here we address this list curation process from a recommender systems perspective. We propose a variety of criteria for generating user list recommendations, based on content analysis, network analysis, and the \"crowdsourcing\" of existing user lists. We demonstrate that these types of criteria are often only successful for datasets with certain characteristics. To resolve this issue, we propose the aggregation of these different \"views\" of a news story on Twitter to produce more accurate user recommendations to support the curation process.","PeriodicalId":258534,"journal":{"name":"Proceedings of the 4th ACM RecSys workshop on Recommender systems and the social web","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129236545","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 22
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