Interactive POI Recommendation: applying a Multi-Armed Bandit framework to characterise and create new models for this scenario

Thiago Silva, N. Silva, Carlos Mito, A. Pereira, Leonardo Rocha
{"title":"Interactive POI Recommendation: applying a Multi-Armed Bandit framework to characterise and create new models for this scenario","authors":"Thiago Silva, N. Silva, Carlos Mito, A. Pereira, Leonardo Rocha","doi":"10.1145/3539637.3557060","DOIUrl":null,"url":null,"abstract":"Nowadays, instead of the traditional batch paradigm where the system trains and predicts a model at scheduled times, new Recommender Systems (RSs) have become interactive models. In this case, the RS should continually recommend the most relevant item(s), receive the user feedback(s), and constantly update itself as a sequential decision model. Thus, the literature has modeled each recommender as a Multi-Armed Bandit (MAB) problem to select new arms (items) at each iteration. However, despite recent advances, MAB models have not yet been studied in some classical scenarios, such as the points-of-interest (POIs) recommendation. For this reason, this work intends to fill this scientific gap, adapting classical MAB algorithms for this context. This process is performed through an interactive recommendation framework called iRec. iRec provides three modules to prepare the dataset, create new recommendation agents, and simulate the interactive scenario. This framework contains several MAB state-of-the-art algorithms, a hyperparameter adjustment module, different evaluation metrics, different visual metaphors to present the results, and statistical validation. By instantiating and adapting iRec to our context, we can assess the quality of different interactive recommenders for the POI recommendation scenario.","PeriodicalId":350776,"journal":{"name":"Proceedings of the Brazilian Symposium on Multimedia and the Web","volume":"89 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the Brazilian Symposium on Multimedia and the Web","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3539637.3557060","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 2

Abstract

Nowadays, instead of the traditional batch paradigm where the system trains and predicts a model at scheduled times, new Recommender Systems (RSs) have become interactive models. In this case, the RS should continually recommend the most relevant item(s), receive the user feedback(s), and constantly update itself as a sequential decision model. Thus, the literature has modeled each recommender as a Multi-Armed Bandit (MAB) problem to select new arms (items) at each iteration. However, despite recent advances, MAB models have not yet been studied in some classical scenarios, such as the points-of-interest (POIs) recommendation. For this reason, this work intends to fill this scientific gap, adapting classical MAB algorithms for this context. This process is performed through an interactive recommendation framework called iRec. iRec provides three modules to prepare the dataset, create new recommendation agents, and simulate the interactive scenario. This framework contains several MAB state-of-the-art algorithms, a hyperparameter adjustment module, different evaluation metrics, different visual metaphors to present the results, and statistical validation. By instantiating and adapting iRec to our context, we can assess the quality of different interactive recommenders for the POI recommendation scenario.
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
交互式POI建议:应用多武装强盗框架来描述和创建此场景的新模型
如今,新的推荐系统(RSs)已经成为交互式模型,而不是传统的批处理范式,即系统在预定的时间内训练和预测模型。在这种情况下,RS应该不断推荐最相关的项目,接收用户反馈,并作为顺序决策模型不断更新自身。因此,文献将每个推荐器建模为一个多臂强盗(MAB)问题,以在每次迭代中选择新的臂(项目)。然而,尽管最近取得了进展,MAB模型尚未在一些经典场景中进行研究,例如兴趣点(poi)推荐。出于这个原因,这项工作打算填补这一科学空白,适应经典的MAB算法在这种情况下。这个过程是通过一个名为iRec的交互式推荐框架执行的。iRec提供了三个模块来准备数据集、创建新的推荐代理和模拟交互场景。该框架包含几个MAB最先进的算法,一个超参数调整模块,不同的评估指标,不同的视觉隐喻来呈现结果,以及统计验证。通过实例化iRec并使其适应我们的上下文,我们可以评估POI推荐场景中不同交互式推荐器的质量。
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 去求助
来源期刊
自引率
0.00%
发文量
0
期刊最新文献
Evaluating Topic Modeling Pre-processing Pipelines for Portuguese Texts A Proposal to Apply SignWriting in IMSC1 Standard for the Next-Generation of Brazilian DTV Broadcasting System Once Learning for Looking and Identifying Based on YOLO-v5 Object Detection I can’t pay! Accessibility analysis of mobile banking apps Should We Translate? Evaluating Toxicity in Online Comments when Translating from Portuguese to English
×
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
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 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 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1