LODBookRec

L. Malecek, Stepán Balcar, Ladislav Peška
{"title":"LODBookRec","authors":"L. Malecek, Stepán Balcar, Ladislav Peška","doi":"10.1145/3326467.3326476","DOIUrl":null,"url":null,"abstract":"In this paper, we present the LODBookRec application. LODBookRec builds on top of Linked Open Data (LOD) knowledge about literature domain and provides information retrieval GUI to conveniently present this knowledge to the end users. As such, LODBookRec aims to contribute towards better utilization of LOD and provide a suitable platform for on-line evaluation of information retrieval methods, especially recommender systems. LODBookRec contains a basic search GUI and several recommendation methods, with the primary focus on item-based recommendations. The results of offline evaluation indicates that content-based recommendations utilizing attribute-based similarity of books provides best item-based recommendations w.r.t. recommendation relevance for both highly popular books as well as long-tail books. However, modification of the original algorithm via maximal margin relevance increases diversity of the recommended lists with a modest relevance penalties.","PeriodicalId":112673,"journal":{"name":"Proceedings of the 9th International Conference on Web Intelligence, Mining and Semantics - WIMS2019","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"LODBookRec\",\"authors\":\"L. Malecek, Stepán Balcar, Ladislav Peška\",\"doi\":\"10.1145/3326467.3326476\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this paper, we present the LODBookRec application. LODBookRec builds on top of Linked Open Data (LOD) knowledge about literature domain and provides information retrieval GUI to conveniently present this knowledge to the end users. As such, LODBookRec aims to contribute towards better utilization of LOD and provide a suitable platform for on-line evaluation of information retrieval methods, especially recommender systems. LODBookRec contains a basic search GUI and several recommendation methods, with the primary focus on item-based recommendations. The results of offline evaluation indicates that content-based recommendations utilizing attribute-based similarity of books provides best item-based recommendations w.r.t. recommendation relevance for both highly popular books as well as long-tail books. However, modification of the original algorithm via maximal margin relevance increases diversity of the recommended lists with a modest relevance penalties.\",\"PeriodicalId\":112673,\"journal\":{\"name\":\"Proceedings of the 9th International Conference on Web Intelligence, Mining and Semantics - WIMS2019\",\"volume\":\"1 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1900-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the 9th International Conference on Web Intelligence, Mining and Semantics - WIMS2019\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/3326467.3326476\",\"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 International Conference on Web Intelligence, Mining and Semantics - WIMS2019","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3326467.3326476","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

摘要

本文章由计算机程序翻译,如有差异,请以英文原文为准。
LODBookRec
In this paper, we present the LODBookRec application. LODBookRec builds on top of Linked Open Data (LOD) knowledge about literature domain and provides information retrieval GUI to conveniently present this knowledge to the end users. As such, LODBookRec aims to contribute towards better utilization of LOD and provide a suitable platform for on-line evaluation of information retrieval methods, especially recommender systems. LODBookRec contains a basic search GUI and several recommendation methods, with the primary focus on item-based recommendations. The results of offline evaluation indicates that content-based recommendations utilizing attribute-based similarity of books provides best item-based recommendations w.r.t. recommendation relevance for both highly popular books as well as long-tail books. However, modification of the original algorithm via maximal margin relevance increases diversity of the recommended lists with a modest relevance penalties.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信