The Journal of Machine Learning Research
Know your neighbors: web spam detection using the web topology
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
Collaborative filtering for orkut communities: discovery of user latent behavior
Proceedings of the 18th international conference on World wide web
Named entity recognition in query
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
Latent dirichlet allocation for tag recommendation
Proceedings of the third ACM conference on Recommender systems
Effectiveness of web search results for genre and sentiment classification
Journal of Information Science
OTM '09 Proceedings of the Confederated International Conferences, CoopIS, DOA, IS, and ODBASE 2009 on On the Move to Meaningful Internet Systems: Part II
Exploring generative models of tripartite graphs for recommendation in social media
Proceedings of the 4th International Workshop on Modeling Social Media
Social Link Prediction in Online Social Tagging Systems
ACM Transactions on Information Systems (TOIS)
Pre-release box-office success prediction for motion pictures
MLDM'13 Proceedings of the 9th international conference on Machine Learning and Data Mining in Pattern Recognition
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In the recent years, the number of social network users has increased dramatically. The resulting amount of data associated with users of social networks has created great opportunities for data mining problems. One data mining problem of interest for social networks is the friendship link prediction problem. Intuitively, a friendship link between two users can be predicted based on their common friends and interests. However, using user interests directly can be challenging, given the large number of possible interests. In the past, approaches that make use of an explicit user interest ontology have been proposed to tackle this problem, but the construction of the ontology proved to be computationally expensive and the resulting ontology was not very useful. As an alternative, we propose a topic modeling approach to the problem of predicting new friendships based on interests and existing friendships. Specifically, we use Latent Dirichlet Allocation (LDA) to model user interests and, thus, we create an implicit interest ontology. We construct features for the link prediction problem based on the resulting topic distributions. Experimental results on several LiveJournal data sets of varying sizes show the usefulness of the LDA features for predicting friendships.