Group Recommendation System for Facebook

Baatarjav, Enkh-Amgalan; Phithakkitnukoon, Santi; Dantu, Ram

doi:10.1007/978-3-540-88875-8_41

Cited by 59 publications

(39 citation statements)

References 11 publications

(9 reference statements)

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Section: Online Social Network: a Useful Source Of Informationmentioning

confidence: 99%

“…We interpreted these results to mean that all of the three ties played certain roles as social filters. Friends and foes are important in the choice of items to consider and allies are important to decide how to vote.Baartarjav, et al [7] presented an exemplary study demonstrating homophily in online space, in particular online groups. In this study based on Facebook data, the authors focused on the personal traits of online group members such as age, gender, religion, living area, political opinions, etc.…”

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confidence: 99%

“…Depending on the discovered characteristics, they recommended a group to join and their recommendation accuracy was 73% on average. Therefore, similar people get socially associated as co-members of the same group [7].Several studies explored social influences in social media applications. Singla and Richardson [109] tested co-relationship between instant messenger logs and the similarity of search queries.…”

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confidence: 99%

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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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Section: Online Social Network: a Useful Source Of Informationmentioning

confidence: 99%

mentioning

confidence: 99%

mentioning

confidence: 99%

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Recommendations Based on Social Links

Lee

Brusilovsky

2018

Social Information Access

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“…In fact, it is possible to extract general groups' interests and then attribute them to users that are members of these groups. In addition, social network groups can be described based on their members' profiles [158]. This example of node clustering is one illustrative example among many that raise users' privacy concerns in social network [159] [160].…”

Section: Privacy Threatsmentioning

confidence: 99%

Privacy in Online Social Networks

Raad

Chbeir

2013

Lecture Notes in Social Networks

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Abstract. Online social networks have become an important part of the online activities on the web and one of the most influencing media. Unconstrained by physical spaces, online social networks offer to web users new interesting means to communicate, interact, and socialize. While these networks make frequent data sharing and inter-user communications instantly possible, privacy-related issues are their obvious much discussed immediate consequences. Although the notion of privacy may take different forms, the ultimate challenge is how to prevent privacy invasion when much personal information is available. In this context, we address privacy-related issues by resorting to social network analysis and link mining techniques. We first describe the fundamental of social networks, their common representations, and the main motivations associated with their use. Afterwards, we particularly show how privacy attacks can build on social network analysis and link mining techniques to reveal user-sensitive information. The chapter concludes with a discussion of some open challenges to address in future privacy-related works.

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“…SNSs sustain highly distributed user-bases and enable the convergence of users with shared interests, mutual trust, and seeking access to similar resources (Athanasopoulos, et al, 2008). Cherished attributes are their user-friendliness, support for flexible communication, and collaborative engagements through memberships of multiple groups (Baatarjav, Phithakkitnukoon, & Dantu, 2008). The enhancement of an architecture of participation and informal knowledge sharing by SNS such as Facebook make them ideal spaces for mediated intellectual engagement.…”

Section: Introductionmentioning

confidence: 99%

Activity theory and technology mediated interaction: Cognitive scaffolding using question-based consultation on Facebook

Rambe

2012

AJET

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Studies that employed activity theory as a theoretical lens for exploring computermediated interaction have not adopted social media as their object of study. However, social media provides lecturers with personalised learning environments for diagnostic and prognostic assessments of student mastery of content and deep learning. The integration of Facebook into educators' pedagogical intentions potentially scaffolds students cognitively, leverages their understanding of content and ameliorates limited mediated learning experiences. Using activity theory as an interpretive framework and a multi-method data construction process involving indepth semi-structured interviews, in-class observations, post observation debriefing and data mining of student and lecturer-generated Facebook postings, the study explored Facebook's potential to scaffold student cogitative processes and promote academic engagement. Findings suggest that the academic value of Facebook is contingent upon the extent of its integration into the pedagogical design of courses, student academic maturity and their level of ICT competence. The unintended effects of Facebook were its reproduction of peer-based academic hierarchies, and its revelation of cognitive tensions and power differentials between academically gifted and cognitively challenged learners during lectures.

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Group Recommendation System for Facebook

Cited by 59 publications

References 11 publications

Recommendations Based on Social Links

Recommendations Based on Social Links

Privacy in Online Social Networks

Activity theory and technology mediated interaction: Cognitive scaffolding using question-based consultation on Facebook

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