R. García, Matthias Bender, Mojisola Erdt, Christoph Rensing, R. Steinmetz
{"title":"基于民俗学的推荐系统的评估框架","authors":"R. García, Matthias Bender, Mojisola Erdt, Christoph Rensing, R. Steinmetz","doi":"10.1145/2365934.2365939","DOIUrl":null,"url":null,"abstract":"FReSET is a new recommender systems evaluation framework aiming to support research on folksonomy-based recommender systems. It provides interfaces for the implementation of folksonomy-based recommender systems and supports the consistent and reproducible offline evaluations on historical data. Unlike other recommender systems framework projects, the emphasis here is on providing a flexible framework allowing users to implement their own folksonomy-based recommender algorithms and pre-processing filtering methods rather than just providing a collection of collaborative filtering implementations. FReSET includes a graphical interface for result visualization and different cross-validation implementations to complement the basic functionality.","PeriodicalId":258534,"journal":{"name":"Proceedings of the 4th ACM RecSys workshop on Recommender systems and the social web","volume":"69 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2012-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"5","resultStr":"{\"title\":\"FReSET: an evaluation framework for folksonomy-based recommender systems\",\"authors\":\"R. García, Matthias Bender, Mojisola Erdt, Christoph Rensing, R. Steinmetz\",\"doi\":\"10.1145/2365934.2365939\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"FReSET is a new recommender systems evaluation framework aiming to support research on folksonomy-based recommender systems. It provides interfaces for the implementation of folksonomy-based recommender systems and supports the consistent and reproducible offline evaluations on historical data. Unlike other recommender systems framework projects, the emphasis here is on providing a flexible framework allowing users to implement their own folksonomy-based recommender algorithms and pre-processing filtering methods rather than just providing a collection of collaborative filtering implementations. FReSET includes a graphical interface for result visualization and different cross-validation implementations to complement the basic functionality.\",\"PeriodicalId\":258534,\"journal\":{\"name\":\"Proceedings of the 4th ACM RecSys workshop on Recommender systems and the social web\",\"volume\":\"69 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2012-09-09\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"5\",\"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/10.1145/2365934.2365939\",\"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/10.1145/2365934.2365939","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
FReSET: an evaluation framework for folksonomy-based recommender systems
FReSET is a new recommender systems evaluation framework aiming to support research on folksonomy-based recommender systems. It provides interfaces for the implementation of folksonomy-based recommender systems and supports the consistent and reproducible offline evaluations on historical data. Unlike other recommender systems framework projects, the emphasis here is on providing a flexible framework allowing users to implement their own folksonomy-based recommender algorithms and pre-processing filtering methods rather than just providing a collection of collaborative filtering implementations. FReSET includes a graphical interface for result visualization and different cross-validation implementations to complement the basic functionality.