Partitioning sparse matrices with eigenvectors of graphs
SIAM Journal on Matrix Analysis and Applications
Fab: content-based, collaborative recommendation
Communications of the ACM
Efficient clustering of high-dimensional data sets with application to reference matching
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
A study of smoothing methods for language models applied to Ad Hoc information retrieval
Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval
The DBLP Computer Science Bibliography: Evolution, Research Issues, Perspectives
SPIRE 2002 Proceedings of the 9th International Symposium on String Processing and Information Retrieval
Authorship Attribution with Support Vector Machines
Applied Intelligence
Clustering and Identifying Temporal Trends in Document Databases
ADL '00 Proceedings of the IEEE Advances in Digital Libraries 2000
Investigating the relationship between language model perplexity and IR precision-recall measures
Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval
The Journal of Machine Learning Research
Item-based top-N recommendation algorithms
ACM Transactions on Information Systems (TOIS)
Email as spectroscopy: automated discovery of community structure within organizations
Communities and technologies
The author-topic model for authors and documents
UAI '04 Proceedings of the 20th conference on Uncertainty in artificial intelligence
ICML '06 Proceedings of the 23rd international conference on Machine learning
Topics over time: a non-Markov continuous-time model of topical trends
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Mining Eclipse Developer Contributions via Author-Topic Models
ICSEW '07 Proceedings of the 29th International Conference on Software Engineering Workshops
Learning implicit user interest hierarchy for context in personalization
Applied Intelligence
DBconnect: mining research community on DBLP data
Proceedings of the 9th WebKDD and 1st SNA-KDD 2007 workshop on Web mining and social network analysis
ArnetMiner: extraction and mining of academic social networks
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Recommendation over a Heterogeneous Social Network
WAIM '08 Proceedings of the 2008 The Ninth International Conference on Web-Age Information Management
Conference Mining via Generalized Topic Modeling
ECML PKDD '09 Proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases: Part I
Understanding research field evolving and trend with dynamic Bayesian networks
PAKDD'07 Proceedings of the 11th Pacific-Asia conference on Advances in knowledge discovery and data mining
Probabilistic latent semantic analysis
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
Empirical analysis of predictive algorithms for collaborative filtering
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
A semantic social network-based expert recommender system
Applied Intelligence
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Conference mining and expert finding are useful academic knowledge discovery problems from an academic recommendation point of view. Group level (GL) topic modeling can provide us with richer text semantics and relationships, which results in denser topics. And denser topics are more useful for academic discovery issues in contrast to Element level (EL) or Document level (DL) topic modeling, which produces sparser topics. Previous methods performed academic knowledge discovery by using network connectivity (only links not text of documents), keywords-based matching (no semantics) or by using semantics-based intrinsic structure of the words presented between documents (semantics at DL), while ignoring semantics-based intrinsic structure of the words and relationships between conferences (semantics at GL). In this paper, we consider semantics-based intrinsic structure of words and relationships presented in conferences (richer text semantics and relationships) by modeling from GL. We propose group topic modeling methods based on Latent Dirichlet Allocation (LDA). Detailed empirical evaluation shows that our proposed GL methods significantly outperformed DL methods for conference mining and expert finding problems.