Blockbusters and Wallflowers: Accurate, Diverse, and Scalable Recommendations with Random Walks

F. Christoffel, B. Paudel, Chris Newell, A. Bernstein
{"title":"Blockbusters and Wallflowers: Accurate, Diverse, and Scalable Recommendations with Random Walks","authors":"F. Christoffel, B. Paudel, Chris Newell, A. Bernstein","doi":"10.1145/2792838.2800180","DOIUrl":null,"url":null,"abstract":"User satisfaction is often dependent on providing accurate and diverse recommendations. In this paper, we explore scalable algorithms that exploit random walks as a sampling technique to obtain diverse recommendations without compromising on accuracy. Specifically, we present a novel graph vertex ranking recommendation algorithm called RP^3_beta that re-ranks items based on 3-hop random walk transition probabilities. We show empirically, that RP^3_beta provides accurate recommendations with high long-tail item frequency at the top of the recommendation list. We also present scalable approximate versions of RP^3_beta and the two most accurate previously published vertex ranking algorithms based on random walk transition probabilities and show that these approximations converge with increasing number of samples.","PeriodicalId":325637,"journal":{"name":"Proceedings of the 9th ACM Conference on Recommender Systems","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2015-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"63","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 9th ACM Conference on Recommender Systems","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/2792838.2800180","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 63

Abstract

User satisfaction is often dependent on providing accurate and diverse recommendations. In this paper, we explore scalable algorithms that exploit random walks as a sampling technique to obtain diverse recommendations without compromising on accuracy. Specifically, we present a novel graph vertex ranking recommendation algorithm called RP^3_beta that re-ranks items based on 3-hop random walk transition probabilities. We show empirically, that RP^3_beta provides accurate recommendations with high long-tail item frequency at the top of the recommendation list. We also present scalable approximate versions of RP^3_beta and the two most accurate previously published vertex ranking algorithms based on random walk transition probabilities and show that these approximations converge with increasing number of samples.
大片和壁花:精确、多样和可扩展的随机漫步推荐
用户满意度通常取决于提供准确和多样化的推荐。在本文中,我们探索了利用随机漫步作为采样技术的可扩展算法,以在不影响准确性的情况下获得不同的推荐。具体来说,我们提出了一种新的图顶点排序推荐算法,称为RP^3_beta,该算法基于3跳随机游走转移概率对项目进行重新排序。我们的经验表明,RP^3_beta在推荐列表的顶部提供了高长尾项目频率的准确推荐。我们还提出了RP^3_beta的可扩展近似版本和两个最准确的先前发表的基于随机游走转移概率的顶点排序算法,并表明这些近似随着样本数量的增加而收敛。
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