Nick Landia, Rachael Mcalister, Donna North, Saikishore Kalloori, Abhishek Srivastava, B. Ferwerda
{"title":"RecSys Challenge 2022数据集:Dressipi 1M时尚会议","authors":"Nick Landia, Rachael Mcalister, Donna North, Saikishore Kalloori, Abhishek Srivastava, B. Ferwerda","doi":"10.1145/3556702.3556779","DOIUrl":null,"url":null,"abstract":"As part of the RecSys Challenge 2022, the Dressipi 1M Fashion Sessions dataset is publicly released. This paper gives an overview of the content and structure of the dataset, as well as explaining the process by which it was constructed. The dataset contains anonymous browsing sessions, a purchase for each session, as well as content data of the items. The content data consists of IDs that represent descriptive fashion characteristics of the items and have been assigned using Dressipi’s human-in-the-loop labelling system. We hope that this dataset will be valuable in recommender systems research beyond the RecSys Challenge and encourage more publications in the fashion domain.","PeriodicalId":141185,"journal":{"name":"Proceedings of the Recommender Systems Challenge 2022","volume":"14 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":"{\"title\":\"RecSys Challenge 2022 Dataset: Dressipi 1M Fashion Sessions\",\"authors\":\"Nick Landia, Rachael Mcalister, Donna North, Saikishore Kalloori, Abhishek Srivastava, B. Ferwerda\",\"doi\":\"10.1145/3556702.3556779\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"As part of the RecSys Challenge 2022, the Dressipi 1M Fashion Sessions dataset is publicly released. This paper gives an overview of the content and structure of the dataset, as well as explaining the process by which it was constructed. The dataset contains anonymous browsing sessions, a purchase for each session, as well as content data of the items. The content data consists of IDs that represent descriptive fashion characteristics of the items and have been assigned using Dressipi’s human-in-the-loop labelling system. We hope that this dataset will be valuable in recommender systems research beyond the RecSys Challenge and encourage more publications in the fashion domain.\",\"PeriodicalId\":141185,\"journal\":{\"name\":\"Proceedings of the Recommender Systems Challenge 2022\",\"volume\":\"14 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2022-09-18\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"3\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the Recommender Systems Challenge 2022\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/3556702.3556779\",\"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 Recommender Systems Challenge 2022","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3556702.3556779","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
As part of the RecSys Challenge 2022, the Dressipi 1M Fashion Sessions dataset is publicly released. This paper gives an overview of the content and structure of the dataset, as well as explaining the process by which it was constructed. The dataset contains anonymous browsing sessions, a purchase for each session, as well as content data of the items. The content data consists of IDs that represent descriptive fashion characteristics of the items and have been assigned using Dressipi’s human-in-the-loop labelling system. We hope that this dataset will be valuable in recommender systems research beyond the RecSys Challenge and encourage more publications in the fashion domain.