Predicting activity attendance in event-based social networks

Abstract

The newly emerging event-based social networks (EBSNs) connect online and offline social interactions, offering a great opportunity to understand behaviors in the cyber-physical space. While existing efforts have mainly focused on investigating user behaviors in traditional social network services (SNS), this paper aims to exploit individual behaviors in EBSNs, which remains an unsolved problem. In particular, our method predicts activity attendance by discovering a set of factors that connect the physical and cyber spaces and influence individual's attendance of activities in EBSNs. These factors, including content preference, context (spatial and temporal) and social influence, are extracted using different models and techniques. We further propose a novel Singular Value Decomposition with Multi-Factor Neighborhood (SVD-MFN) algorithm to predict activity attendance by integrating the discovered heterogeneous factors into a single framework, in which these factors are fused through a neighborhood set. Experiments based on real-world data from Douban Events demonstrate that the proposed SVD-MFN algorithm outperforms the state-of-the-art prediction methods.

Exploring Social Media for Event Attendance

“…The main aspects investigated in the literature are: (1) prediction of events attendance in EventBased Social Networks (EBSN) [7], [8], [9]; (2) recommendation of events to users [10], [11]; and, (3) estimation of the number of attendees in a given event [12]. Du et al [7] analyse an EBSN to predict users' attendance by taking into account the content, the spatial and temporal context, the users' preferences and their social influence. Zhang et al [8] propose a supervised learning model to predict event attendance based on semantic, temporal, and spatial features.…”

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

“…Recently, some other issues of EBSNs have been studied. Some research, such as [5,9,32], train datasets of EBSNs to derive learning models to recommend events to potential users. However, these works just make recommendation rather than optimizing a global arrangement, which is our goal.…”

Participants Ranking Algorithm for Crowdsensing in Mobile Communication

“…A formal way to represent the MCS (mobile Crowd sensing) is: Mobile Crowd Sensing (MCS) presents a new sensing model, which is based on the power of mobile devices. The absolute number of user companioned devices such as mobile phones, wearable devices and smart vehicles so on [1,2] and their inherent mobility empowers a new and fast-growing sensing paradigm that empowers ordinary citizens to contribute data sensed or generated from their mobile devices, aggregates and fuses the data in the cloud for crowd intelligence extraction and people-centric service delivery [2,3]. Mobile crowd sensing (MCS) permits a huge amount of mobile phone users that share local knowledge (e.g., local information, ambient context, noise level, and traffic conditions) collected by their sensor-enhanced devices.…”