Fashion Recommendation with a real Recommender System Flow

Qi Zhang, Guohao Cai, Wei Guo, Yiqiu Han, Zhenhua Dong, Ruiming Tang, Liangbi Li
{"title":"Fashion Recommendation with a real Recommender System Flow","authors":"Qi Zhang, Guohao Cai, Wei Guo, Yiqiu Han, Zhenhua Dong, Ruiming Tang, Liangbi Li","doi":"10.1145/3556702.3556792","DOIUrl":null,"url":null,"abstract":"In this technical report, we present our solution of RecSys Challenge 2022 focusing on the fashion recommendation. We produce recommendations in two steps: (i) the retrieval step, which generates a candidate item set based on multiple cheap-to-compute strategies; (ii) the ranking step: which rearranges the candidate items with a richer set of features. Specifically, we conduct various strategies to retrieve as many positive samples as possible in retrieval step and obtain the retrieval scores from these retrieval channels meanwhile. Then these scores along with some extracted features are involved in the ranking stage for modeling to generate the purchase prediction. In the final submission, we use six effective retrieval strategies in retrieval step and ensemble five ranking models by taking average of their outputs. Using our method, our team doubleQ achieved MRR 0.2013 on final test set which wins the 10 place, and the solution codes are available via https://github.com/doubleQ2018/recsys-challenge-2022.","PeriodicalId":141185,"journal":{"name":"Proceedings of the Recommender Systems Challenge 2022","volume":"96 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the Recommender Systems Challenge 2022","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3556702.3556792","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

Abstract

In this technical report, we present our solution of RecSys Challenge 2022 focusing on the fashion recommendation. We produce recommendations in two steps: (i) the retrieval step, which generates a candidate item set based on multiple cheap-to-compute strategies; (ii) the ranking step: which rearranges the candidate items with a richer set of features. Specifically, we conduct various strategies to retrieve as many positive samples as possible in retrieval step and obtain the retrieval scores from these retrieval channels meanwhile. Then these scores along with some extracted features are involved in the ranking stage for modeling to generate the purchase prediction. In the final submission, we use six effective retrieval strategies in retrieval step and ensemble five ranking models by taking average of their outputs. Using our method, our team doubleQ achieved MRR 0.2013 on final test set which wins the 10 place, and the solution codes are available via https://github.com/doubleQ2018/recsys-challenge-2022.
时尚推荐与一个真正的推荐系统流程
在这份技术报告中,我们提出了我们的RecSys挑战2022的解决方案,重点是时尚推荐。我们分两步提出建议:(i)检索步骤,该步骤基于多个廉价计算策略生成候选项集;(ii)排序步骤:以更丰富的特征集重新排列候选项目。具体而言,我们在检索步骤中采取各种策略,尽可能多地检索阳性样本,同时从这些检索通道中获取检索分数。然后,这些分数和一些提取的特征一起进入排序阶段,用于建模以生成购买预测。在最后的提交中,我们在检索步骤中使用了六种有效的检索策略,并通过对它们的输出进行平均来集成五种排序模型。使用我们的方法,我们的团队doubleQ在最终测试集上的MRR达到了0.2013,获得了10名,解决方案代码可通过https://github.com/doubleQ2018/recsys-challenge-2022获得。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
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:604180095
Book学术官方微信