Mining Human Mobility in Location-Based Social Networks (Synthesis Lectures on Data Mining and Knowledge Discover)
暫譯: 挖掘基於位置的社交網絡中的人類移動性(數據挖掘與知識發現綜合講座)
Huiji Gao, Haun Liu
- 出版商: Morgan & Claypool
- 出版日期: 2015-04-01
- 售價: $1,620
- 貴賓價: 9.5 折 $1,539
- 語言: 英文
- 頁數: 115
- 裝訂: Paperback
- ISBN: 162705412X
- ISBN-13: 9781627054126
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相關分類:
Data-mining
海外代購書籍(需單獨結帳)
商品描述
In recent years, there has been a rapid growth of location-based social networking services, such as Foursquare and Facebook Places, which have attracted an increasing number of users and greatly enriched their urban experience. Typical location-based social networking sites allow a user to ""check in"" at a real-world POI (point of interest, e.g., a hotel, restaurant, theater, etc.), leave tips toward the POI, and share the check-in with their online friends. The check-in action bridges the gap between real world and online social networks, resulting in a new type of social networks, namely location-based social networks (LBSNs). Compared to traditional GPS data, location-based social networks data contains unique properties with abundant heterogeneous information to reveal human mobility, i.e., ""when and where a user (who) has been to for what,"" corresponding to an unprecedented opportunity to better understand human mobility from spatial, temporal, social, and content aspects. The mining and understanding of human mobility can further lead to effective approaches to improve current location-based services from mobile marketing to recommender systems, providing users more convenient life experience than before. This book takes a data mining perspective to offer an overview of studying human mobility in location-based social networks and illuminate a wide range of related computational tasks. It introduces basic concepts, elaborates associated challenges, reviews state-of-the-art algorithms with illustrative examples and real-world LBSN datasets, and discusses effective evaluation methods in mining human mobility. In particular, we illustrate unique characteristics and research opportunities of LBSN data, present representative tasks of mining human mobility on location-based social networks, including capturing user mobility patterns to understand when and where a user commonly goes (location prediction), and exploiting user preferences and location profiles to investigate where and when a user wants to explore (location recommendation), along with studying a user's check-in activity in terms of why a user goes to a certain location.
商品描述(中文翻譯)
近年來,基於位置的社交網路服務(如 Foursquare 和 Facebook Places)迅速增長,吸引了越來越多的用戶,並大大豐富了他們的城市體驗。典型的基於位置的社交網路網站允許用戶在現實世界的興趣點(POI,例如酒店、餐廳、劇院等)進行「簽到」,留下對該 POI 的建議,並與他們的在線朋友分享簽到信息。簽到行為彌合了現實世界與在線社交網路之間的鴻溝,形成了一種新型的社交網路,即基於位置的社交網路(LBSNs)。與傳統的 GPS 數據相比,基於位置的社交網路數據具有獨特的特性,包含豐富的異質信息,以揭示人類的流動性,即「用戶(誰)在何時何地去過什麼」,這為從空間、時間、社會和內容方面更好地理解人類流動性提供了前所未有的機會。對人類流動性的挖掘和理解可以進一步導致有效的方法來改善當前的基於位置的服務,從移動行銷到推薦系統,為用戶提供比以往更便捷的生活體驗。本書從數據挖掘的角度出發,提供了在基於位置的社交網路中研究人類流動性的概述,並闡明了一系列相關的計算任務。它介紹了基本概念,詳細說明了相關挑戰,回顧了最先進的算法,並提供了說明性示例和真實世界的 LBSN 數據集,討論了挖掘人類流動性的有效評估方法。特別是,我們闡述了 LBSN 數據的獨特特徵和研究機會,展示了在基於位置的社交網路上挖掘人類流動性的代表性任務,包括捕捉用戶流動模式以了解用戶通常何時何地去(位置預測),以及利用用戶偏好和位置檔案來調查用戶想要探索的地點和時間(位置推薦),同時研究用戶的簽到活動,以了解用戶為何前往某個特定地點。