Finding regions of interest using location based social media

Shang, Shuo; Guo, Danhuai; Liu, Jiajun; Zheng, Kai; Wen, Ji-Rong

doi:10.1016/j.neucom.2015.06.086

Cited by 33 publications

(9 citation statements)

References 25 publications

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“…First, it is of interest to integrate spatial trajectory data [24, 26-30, 34, 37-39, 64, 65] with text data and to conduct novel spatio-textual trajectory search, recommendation, and analysis studies. Second, it is of interest to use POI data and geo-tagged social media data to discover significant locations/regions [13,41,[45][46][47][48]52,60]. Third, it is of interest to study integrate text data to routing problem [35,36,40,[42][43][44]56,59,66] and to study spatio-textual routing problem.…”

Section: Related Workmentioning

confidence: 99%

Approximate spatio-temporal top-k publish/subscribe

Chen

Shang

2018

World Wide Web

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Location-based publish/subscribe plays a significant role in mobile information disseminations. In this light, we propose and study a novel problem of processing location-based top-k subscriptions over spatio-temporal data streams. We define a new type of approximate location-based top-k subscription, Approximate Temporal Spatial-Keyword Top-k (ATSK) Subscription, that continuously feeds users with relevant spatio-temporal messages by considering textual similarity, spatial proximity, and information freshness. Different from existing location-based top-k subscriptions, Approximate Temporal Spatial-Keyword Top-k (ATSK) Subscription can automatically adjust the triggering condition by taking the triggering score of other subscriptions into account. The group filtering efficacy can be substantially improved by sacrificing the publishing result quality with a bounded guarantee. We conduct extensive experiments on two real datasets to demonstrate the performance of the developed solutions.

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Section: Related Workmentioning

confidence: 99%

Approximate spatio-temporal top-k publish/subscribe

Chen

Shang

2018

World Wide Web

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“…However, the QoS values may contain personal sensitive information so that users are unwilling to share them directly. Some researches point out that some QoS properties like response time and availability highly related to the users' physical locations [3,51,43,15,62,61,63,64], which means that the users' location could be deduced from the QoS information.…”

Section: Related Workmentioning

confidence: 99%

Differential private collaborative Web services QoS prediction

Liu

Shen

et al. 2018

World Wide Web

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Collaborative Web services QoS prediction has proved to be an important tool to estimate accurately personalized QoS experienced by individual users, which is beneficial for a variety of operations in the service ecosystem, such as service selection, composition and recommendation. While a number of achievements have been attained on the study of improving the accuracy of collaborative QoS prediction, little work has been done for protecting user privacy in this process. In this paper, we propose a privacy-preserving collaborative QoS prediction framework which can protect the private data of users while retaining the ability of generating accurate QoS prediction. We introduce differential privacy, a rigorous and provable privacy model, into the process of collaborative QoS prediction. We first present DPS, a method that disguises a user's observed QoS values by applying differential privacy to the user's QoS data directly. We show how to integrate DPS with two representative collaborative QoS prediction approaches. To improve the utility of the disguised QoS data, we present DPA, another QoS disguising method which first aggregates a user's QoS data before adding noise to achieve differential privacy. We evaluate the proposed methods by conducting extensive experiments on a real world Web services QoS dataset. Experimental results show our approach is feasible in practice.

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“…The dimension of place helps link the gap between the physical world and virtual social networking services [21]. On the other hand, public participation activities utilized from geo-social networks capabilities are used to solve urban spatial problems [11,22].…”

Section: Geo-social Networkmentioning

confidence: 99%

A Novel Approach to Site Selection: Collaborative Multi-Criteria Decision Making through Geo-Social Network (Case Study: Public Parking)

Samani

Karimi

Alesheikh

2018

IJGI

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There are many potential factors that are involved in the decision making process of site selection, which makes it a challenging issue. This paper addresses the collaborative decision making concept through a geo-social network to predict site selection for public parking in Tehran, Iran. The presented approach utilized the analytic hierarchy process (AHP) as a multi-criteria decision method (MCDM) for weighting the criteria, which was completed in two stages; once by 50 experts, and then by three different levels of users, including 50 experts, 25 urban managers, and 150 pubic citizens, with respect to the case study area. The fuzzy majority method aggregates the archived results of AHP to determine the preferred locations that are suitable for public parking. The proposed method was implemented using a telegram bot platform. Two main advantages of the collaborative decision making scenario for public urban site selection are the fair distribution of the selected locations and the high satisfaction of users, which increased from 65% to 85%. This study presents an application for site selection based on multi-criteria decision making in a geo-social network context.

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Finding regions of interest using location based social media

Cited by 33 publications

References 25 publications

Approximate spatio-temporal top-k publish/subscribe

Approximate spatio-temporal top-k publish/subscribe

Differential private collaborative Web services QoS prediction

A Novel Approach to Site Selection: Collaborative Multi-Criteria Decision Making through Geo-Social Network (Case Study: Public Parking)

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