Predicting Friendship Links in Social Networks Using a Topic Modeling Approach

Parimi, Rohit; Caragea, Doina

doi:10.1007/978-3-642-20847-8_7

Cited by 31 publications

(13 citation statements)

References 9 publications

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“…In contrast to our work, their focus was to learn shared topics of interest among friends. Perhaps the work closest to ours is that of Parimi et al [24]. Their hierarchical system exploits latent user interests based on user profiles, treating users as documents.…”

Section: Related Workmentioning

confidence: 97%

Exploring generative models of tripartite graphs for recommendation in social media

Chelmis

Prasanna

2013

Proceedings of the 4th International Workshop on Modeling Social Media

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As social media sites grow in popularity, tagging has naturally emerged as a method of searching, categorizing and filtering online information, especially multimedia content. The unrestricted vocabulary users choose from to annotate content however, has often lead to an explosion of the size of space in which search is performed. This paper is concerned with investigating generative models of social annotations, and testing their efficiency with respect to two information consumption oriented tasks. One task considers recommending new tags (similarly new resources) for new, previously unknown users. We use perplexity as a standard measure for estimating the generalization performance of a probabilistic model. The second task is aimed at recommending new users to connect with. In this task, we examine which users' activity is most discriminative in predicting social ties: annotation (i.e. tags), resource usage (i.e. artists), or collective annotation of resources altogether. For the second task, we propose a framework to integrate the modeling of social annotations with network proximity. The proposed approach consists of two steps: (1) discovering salient topics that characterize users, resources and annotations; and (2) enhancing the recommendation power of such models by incorporating social clues from the immediate neighborhood of users. In particular, we propose four classification schemes for social link recommendation, which we evaluate on a real-world dataset from Last.fm. Our results demonstrate significant improvements over traditional approaches.

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Section: Related Workmentioning

confidence: 97%

Exploring generative models of tripartite graphs for recommendation in social media

Chelmis

Prasanna

2013

Proceedings of the 4th International Workshop on Modeling Social Media

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“…Even though our approach is similar in spirit, our user modeling radically differs. Perhaps the work closest to ours is that of Parimi and Caragea [2011]. Their hierarchical system exploits latent user interests based on user profiles, treating users as documents.…”

Section: Related Workmentioning

confidence: 98%

Social Link Prediction in Online Social Tagging Systems

Chelmis

Prasanna

2013

ACM Trans. Inf. Syst.

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Social networks have become a popular medium for people to communicate and distribute ideas, content, news, and advertisements. Social content annotation has naturally emerged as a method of categorization and filtering of online information. The unrestricted vocabulary users choose from to annotate content has often lead to an explosion of the size of space in which search is performed. In this article, we propose latent topic models as a principled way of reducing the dimensionality of such data and capturing the dynamics of collaborative annotation process. We propose three generative processes to model latent user tastes with respect to resources they annotate with metadata. We show that latent user interests combined with social clues from the immediate neighborhood of users can significantly improve social link prediction in the online music social media site Last.fm. Most link prediction methods suffer from the high class imbalance problem, resulting in low precision and/or recall. In contrast, our proposed classification schemes for social link recommendation achieve high precision and recall with respect to not only the dominant class (nonexistence of a link), but also with respect to sparse positive instances, which are the most vital in social tie prediction.

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“…An approach built upon specific user interest ontology has been proposed to deal with the link prediction problem. They present a topic modeling method to predict new friendship links based on common interests and existing friendships [29 ].…”

Section: Related Workedmentioning

confidence: 99%