Similarity measures in scientometric research: the Jaccard index versus Salton's cosine formula
Information Processing and Management: an International Journal
Communications of the ACM
The Journal of Machine Learning Research
Link prediction approach to collaborative filtering
Proceedings of the 5th ACM/IEEE-CS joint conference on Digital libraries
Selectively acquiring ratings for product recommendation
Proceedings of the ninth international conference on Electronic commerce
Online Discussion Participation Prediction Using Non-negative Matrix Factorization
WI-IATW '07 Proceedings of the 2007 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology - Workshops
Movies Recommendation Networks as Bipartite Graphs
ICCS '08 Proceedings of the 8th international conference on Computational Science, Part II
Proceedings of the 18th international conference on World wide web
Predicting response to political blog posts with topic models
NAACL '09 Proceedings of Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics
Trend analysis model: trend consists of temporal words, topics, and timestamps
Proceedings of the fourth ACM international conference on Web search and data mining
Why do we converse on social media?: an analysis of intrinsic and extrinsic network factors
WSM '11 Proceedings of the 3rd ACM SIGMM international workshop on Social media
Empirical analysis of predictive algorithms for collaborative filtering
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
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Predicting whether a user will be participating in a thread has broad applications, such as thread recommendation and ranking. In an extremist forum, knowing which user will be interested to join a particular thread with sensitive or threatening information is also important for security agent to prevent or prepare for any potential outbreak of crisis. Traditional methods employed a bipartite graph to represent user-thread relationships and predict potential users for a new coming thread based on user similarities. In this paper, we propose a User Interest and Topic Detection model to extract topics and trends from a document corpus and also discover users' interests toward these trends. Information of user interest is then used to predict potential information consumers for a given thread. Experiments conducted in the Dark Web dataset showed the effectiveness of our approach; especially when we have limited information about who have already participated in an existing new thread.