Context-Aware Event Recommendation in Event-based Social Networks

Macedo, Augusto Q. de; Marinho, Leandro Balby; Santos, Rodrygo L. T.

doi:10.1145/2792838.2800187

Cited by 175 publications

(100 citation statements)

References 15 publications

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“…Events in social media have been extensively studied. 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.…”

Section: Related Workmentioning

confidence: 99%

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Exploring Social Media for Event Attendance

Lira

Macdonald

Ounis

et al. 2017

Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017

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Abstract-Large popular events are nowadays well reflected in social media fora (e.g. Twitter), where people discuss their interest in participating in the events. In this paper we propose to exploit the content of non-geotagged posts in social media to build machine-learned classifiers able to infer users' attendance of large events in three temporal periods: before, during and after an event. The categories of features used to train the classifier reflect four different dimensions of social media: textual, temporal, social, and multimedia content. We detail the approach followed to design the feature space and report on experiments conducted on two large music festivals in the UK, namely the VFestival and Creamfields events. Our attendance classifier attains very high accuracy with the highest result observed for the Creamfields dataset ∼87% accuracy to classify users that will participate in the event.

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Section: Related Workmentioning

confidence: 99%

“…Within the second category, event recommendation, [10], [11] and [13] address the challenge of recommending events within event-based social networks (EBSNs). Each of these approaches is challenged by the cold-start problem, and recommendation evidence may resort to the events that are geographically closest [10].…”

Section: Related Workmentioning

confidence: 99%

Exploring Social Media for Event Attendance

Lira

Macdonald

Ounis

et al. 2017

Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017

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“…In this way, they enriched the users' binary preferences into multi-dimensional preferences. Macedo, et al [82] considered not only users' event attendance records but also various contextual information of events such as topics of events, the locations, and the temporal information. Sun, et al [116] and Lee & Brusilovsky [71,74] used social tags to improve the quality of bookmark-based recommendations.…”

Section: Input Data Types Of Social Link-based Recommendationsmentioning

confidence: 99%

Recommendations Based on Social Links

Lee

Brusilovsky

2018

Social Information Access

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Abstract. The goal of this chapter is to give an overview of recent works on the development of social link-based recommender systems and to offer insights on related issues and the future direction. Among several branches of social recommendations, specifically, this chapter focuses on recommendations, which are founded on users' self-defined (i.e., explicit) social links and target to suggest desirable items, not social links of interests. This chapter provides a brief overview over the technical needs for social link-based recommendations and the studies explaining the viability of users' social networks as useful information sources from a social science point of view. Then, the in-depth understanding of existing recommendations based on users' social links will be addressed. Lastly, various issues and future directions of social link-based recommendation research are put forward. IntroductionThe remarkable popularity of social media encourages web users to participate in various online activities and to generate data on an unprecedented scale. Given exponentially growing social media data volumes, personalized recommendation technologies have proven the effectiveness as a solution of the information overload problem; have offered positive user experiences with more relevant contents; and have presented competitive advantage to the social media business [119]. As predictive analysis of online users' tastes, recommendation technology adaptively filters out unnecessary data and selectively chooses presumably favorable information according to users' preferences. At odds with the original principle of personalized recommendations, which is to give users control of information access, however, the typical and most popular collaborative filtering recommendation technology performs all steps autonomously and allows no room for users to get involved in information personalization of their own. The lacking user involvement causes several problems (illustrated in Section 12.3.1), and many researchers have called attention to solutions to improve the quality of typical recommendation technology and to cope with the relevant problems. Among the several streams of research to develop the solutions, one important evolution is to take advantage of users' self-defined online social networks and proposes to generate recommendations based on users' social linkssocial link-based recommendations. The idea to utilize users' online social links as a foundation of recommendation is undoubtedly spurred by the recent phenomenon of online social networks.Compared with the era when web users stayed in isolation, a number of social media systems have been adapting online sociability, helping users to find people of interest, and as well as encouraging them to socially associate with people of interest. In fact, web users' active and eager participation on social media are intrinsically motivated by not only personal desire for information management and knowledge acquisition but also social desire for engagement and communication with...

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“…based on tensor factorization [24,23] incorporate time [26,45,9] or location [14,28] as additional parameters to encode contextual information. In addition, some advocate to model the local social environment [43,30] and propose strategies to improve recommendations in households with multiple users sharing a single device [38].…”

Section: Contextual Factors In Recommendersmentioning

confidence: 99%

Countering Contextual Bias in TV Watching Behavior

Lorenz

Jing

Lommatzsch

et al. 2017

Proceedings of the 2017 ACM International Conference on Interactive Experiences for TV and Online Video

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Context-awareness has become a critical factor in improving the predictions of user interest in modern online TV recommendation systems. In addition to individual user preferences, existing context-aware approaches such as tensor factorization incorporate system-level contextual bias to increase predicting accuracy. We analyzed a user interaction dataset from a WebTV platform, and identified that such contextual bias creates a skewed selection of recommended programs which ultimately locks users in a filter bubble. To address this issue, we introduce the Twitter social stream as a source of external context to extend the choice with items related to social media events. We apply two trend indicators, Trend Momentum and SigniScore, to the Twitter histories of relevant programs. The evaluation reveals that Trend Momentum outperforms SigniScore and signalizes 96% of all peaks ahead of time regarding the selected candidate program titles.

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Context-Aware Event Recommendation in Event-based Social Networks

Cited by 175 publications

References 15 publications

Exploring Social Media for Event Attendance

Exploring Social Media for Event Attendance

Recommendations Based on Social Links

Countering Contextual Bias in TV Watching Behavior

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