On socio-spatial group query for location-based social networks

Yang, De-Nian; Shen, Chih-Ya; Lee, Wang-Chien; Chen, Ming-Syan

doi:10.1145/2339530.2339679

Cited by 112 publications

(60 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Cho et al [7] studied the problem of using social relationships to explain human movement. Yang et al [36] introduced a Socio-Spatial Group Query (SSGQ) that retrieves a group of users who have certain social connection strength and the sum of their spatial distances is minimized. Most recently, a general framework for Geo-Social query processing that supports Range Friends (RF), Nearest Friends (NF), and Nearest Star Group (NSG) queries was proposed in [4].…”

Section: Related Workmentioning

confidence: 99%

“…To the best of our knowledge, there is no previous work on clustering GeoSN places. While there exists a significant body of research on analyzing and querying GeoSN data [4,7,27,28,36,37], most of these works are centered around users; i.e., they study user behavior, user link prediction or recommendation, or the evaluation of user queries. Thus, the places and checkins are only regarded as some auxiliary information to facilitate user-centered analysis.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Density-based place clustering in geo-social networks

Shi

Mamoulis

et al. 2014

Proceedings of the 2014 ACM SIGMOD International Conference on Management of Data

View full text Add to dashboard Cite

Spatial clustering deals with the unsupervised grouping of places into clusters and finds important applications in urban planning and marketing. Current spatial clustering models disregard information about the people who are related to the clustered places. In this paper, we show how the density-based clustering paradigm can be extended to apply on places which are visited by users of a geo-social network. Our model considers both spatial information and the social relationships between users who visit the clustered places. After formally defining the model and the distance measure it relies on, we present efficient algorithms for its implementation, based on spatial indexing. We evaluate the effectiveness of our model via a case study on real data; in addition, we design two quantitative measures, called social entropy and community score to evaluate the quality of the discovered clusters. The results show that geo-social clusters have special properties and cannot be found by applying simple spatial clustering approaches. The efficiency of our index-based implementation is also evaluated experimentally.

show abstract

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Density-based place clustering in geo-social networks

Shi

Mamoulis

et al. 2014

Proceedings of the 2014 ACM SIGMOD International Conference on Management of Data

View full text Add to dashboard Cite

show abstract

“…In other words, these works do not target at the arrangement optimization problem, which is the main difference with our work. Moreover, [1,31] study the problems of query processing over LBSNs. The two studies only consider two factors, the location information and friendship, and ignore the similarity of attributes between activities and users.…”

Section: Location-based Social Networkmentioning

confidence: 99%

Bottleneck-aware arrangement over event-based social networks: the max-min approach

2015

View full text Add to dashboard Cite

With the popularity of mobile computing and social media, various kinds of online event-based social network (EBSN) platforms, such as Meetup, Plancast and Whova, are gaining in prominence. A fundamental task of managing EBSN platforms is to recommend suitable social events to potential users according to the following three factors: spatial locations of events and users, attribute similarities between events and users, and friend relationships among users. However, none of the existing approaches considers all the aforementioned influential factors when they recommend users to proper events. Furthermore, the existing recommendation strategies neglect the bottleneck cases of the global recommendation. Thus, it is impossible for the existing recommendation solutions to be fair in real-world scenarios. In this paper, we first formally define the problem of bottleneck-aware social event arrangement (BSEA), which is proven to be NP-hard. To solve the BSEA problem approximately, Electronic supplementary material The online version of this article (World Wide Web we devise two greedy heuristic algorithms, Greedy and Random+Greedy, and a localsearch-based optimization technique. In particular, the Greedy algorithm is more effective but less efficient than the Random+Greedy algorithm in most cases. Moreover, a variant of the BSEA problem, called the Extended BSEA problem, is studied, and the above solutions can be extended to address this variant easily. Finally, we conduct extensive experiments on real and synthetic datasets which verify the efficiency and effectiveness of our proposed algorithms.

show abstract

“…We consider these factors in this paper in terms of decaying visiting probability with distance. Socio-spatial group query [18] is similar to team formation, which attempts to minimize total distance from group members to the rally point; however, it does not consider the social relationship and item coverage. In addition, we focus on an applicationoriented framework unlike team formation, which focuses on theory issues of operational research.…”

Section: B Team Formationmentioning

confidence: 99%

Participant Selection for Offline Event Marketing Leveraging Location-Based Social Networks

Zhang

et al. 2015

IEEE Trans. Syst. Man Cybern, Syst.

View full text Add to dashboard Cite

Offline event marketing invites people to participate in a sponsored gathering, thus allowing marketers to have face-to-face, direct, and close contact with their current and potential customers. This paper presents a framework that supports marketers in improving marketing effectiveness by carefully selecting invitees to such sponsored offline events by leveraging location-based social networks. In particular, we first transform the participant selection task into a combinatorial optimization problem. Second, we propose a marketing effect quantitative model that considers the distance and overlapping social influence. Third, we introduce algorithms to determine a participant team that can maximize the marketing effect while fulfilling the scale and item coverage constraints. We finally evaluate the effectiveness of the framework and validate the proposed marketing effect of the quantitative model with real-world data.Index Terms-Location-based social networks, offline event marketing, participant selection.

show abstract

On socio-spatial group query for location-based social networks

Cited by 112 publications

References 13 publications

Density-based place clustering in geo-social networks

Density-based place clustering in geo-social networks

Bottleneck-aware arrangement over event-based social networks: the max-min approach

Participant Selection for Offline Event Marketing Leveraging Location-Based Social Networks

Contact Info

Product

Resources

About