标题:Venue Prediction for Social Images by Exploiting Rich Temporal Patterns in LBSNs
作者:Chen, Jingyuan; He, Xiangnan; Song, Xuemeng; Zhang, Hanwang; Nie, Liqiang; Chua, Tat-Seng
通讯作者:Chen, Jingyuan;Chen, JY
作者机构:[Chen, Jingyuan; He, Xiangnan; Chua, Tat-Seng] Natl Univ Singapore, Sch Comp, Singapore, Singapore.; [Song, Xuemeng; Nie, Liqiang] Shandong Univ, Sc 更多
会议名称:24th International Conference on MultiMedia Modeling (MMM)
会议日期:FEB 05-07, 2018
来源:MULTIMEDIA MODELING, MMM 2018, PT II
出版年:2018
卷:10705
页码:327-339
DOI:10.1007/978-3-319-73600-6_28
关键词:Venue prediction; Transition pattern; Temporal pattern
摘要:Location (or equivalently, "venue") is a crucial facet of user generated images in social media (aka. social images) to describe the events of people's daily lives. While many existing works focus on predicting the venue category based on image content, we tackle the grand challenge of predicting the specific venue of a social image. Simply using the visual content of a social image is insufficient for this purpose due its high diversity. In this work, we leverage users' check-in histories in location-based social networks (LBSNs), which contain rich temporal movement patterns, to complement the limitations of using visual signals alone. In particular, we explore the transition patterns on successive check-ins and periodical patterns on venue categories from users' check-in behaviors in Foursquare. For example, users tend to check-in to cinemas nearby after having meals at a restaurant (transition patterns), and frequently check-in to churches on every Sunday morning (periodical patterns). To incorporate such rich temporal patterns into the venue prediction process, we propose a generic embedding model that fuses the visual signal from image content and various temporal signal from LBSN check-in histories. We conduct extensive experiments on Instagram social images, demonstrating that by properly leveraging the temporal patterns latent in Foursquare check-ins, we can significantly boost the accuracy of venue prediction.
收录类别:CPCI-S;EI;SCOPUS
资源类型:会议论文;期刊论文
原文链接:https://www.scopus.com/inward/record.uri?eid=2-s2.0-85042074730&doi=10.1007%2f978-3-319-73600-6_28&partnerID=40&md5=974cbcd9ebc981c1b9c3fef699833446
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