{"title":"Top-N推荐评估的最佳实践:候选集抽样和统计推断技术","authors":"Ngozi Ihemelandu","doi":"10.1145/3511808.3557816","DOIUrl":null,"url":null,"abstract":"Top-N recommendation evaluation experiments are complex, with many decisions needed. These decisions are often made inconsistently, and we don't have clear best practices for many of them. The goal of this project, is to identify, substantiate, and document best practices to improve evaluations.","PeriodicalId":389624,"journal":{"name":"Proceedings of the 31st ACM International Conference on Information & Knowledge Management","volume":"603 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Best Practices for Top-N Recommendation Evaluation: Candidate Set Sampling and Statistical Inference Techniques\",\"authors\":\"Ngozi Ihemelandu\",\"doi\":\"10.1145/3511808.3557816\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Top-N recommendation evaluation experiments are complex, with many decisions needed. These decisions are often made inconsistently, and we don't have clear best practices for many of them. The goal of this project, is to identify, substantiate, and document best practices to improve evaluations.\",\"PeriodicalId\":389624,\"journal\":{\"name\":\"Proceedings of the 31st ACM International Conference on Information & Knowledge Management\",\"volume\":\"603 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2022-10-17\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the 31st ACM International Conference on Information & Knowledge Management\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/3511808.3557816\",\"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 31st ACM International Conference on Information & Knowledge Management","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3511808.3557816","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Best Practices for Top-N Recommendation Evaluation: Candidate Set Sampling and Statistical Inference Techniques
Top-N recommendation evaluation experiments are complex, with many decisions needed. These decisions are often made inconsistently, and we don't have clear best practices for many of them. The goal of this project, is to identify, substantiate, and document best practices to improve evaluations.
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