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
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
Algorithms for estimating relative importance in networks
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Challenges in enterprise search
ADC '04 Proceedings of the 15th Australasian database conference - Volume 27
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
Formal models for expert finding in enterprise corpora
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
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
Proceedings of the 16th international conference on World Wide Web
Broad expertise retrieval in sparse data environments
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
Expertise modeling for matching papers with reviewers
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Short communication: Variable space hidden Markov model for topic detection and analysis
Knowledge-Based Systems
On the value of temporal information in information retrieval
ACM SIGIR Forum
ArnetMiner: extraction and mining of academic social networks
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Multinomial mixture model with feature selection for text clustering
Knowledge-Based Systems
Expertise Search in a Time-Varying Social Network
WAIM '08 Proceedings of the 2008 The Ninth International Conference on Web-Age Information Management
Co-authorship networks in the digital library research community
Information Processing and Management: an International Journal - Special issue: Infometrics
A mixture model for expert finding
PAKDD'08 Proceedings of the 12th 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
Exploiting explicit semantics-based grouping for author interest finding
APWeb'11 Proceedings of the 13th Asia-Pacific web conference on Web technologies and applications
ImpactWheel: Visual Analysis of the Impact of Online News
WI-IAT '11 Proceedings of the 2011 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology - Volume 01
Using time topic modeling for semantics-based dynamic research interest finding
Knowledge-Based Systems
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This paper addresses the problem of semantics-based temporal expert finding, which means identifying a person with given expertise for different time periods. For example, many real world applications like reviewer matching for papers and finding hot topics in newswire articles need to consider time dynamics. Intuitively there will be different reviewers and reporters for different topics during different time periods. Traditional approaches used graph-based link structure by using keywords based matching and ignored semantic information, while topic modeling considered semantics-based information without conferences influence (richer text semantics and relationships between authors) and time information simultaneously. Consequently they result in not finding appropriate experts for different time periods. We propose a novel Temporal-Expert-Topic (TET) approach based on Semantics and Temporal Information based Expert Search (STMS) for temporal expert finding, which simultaneously models conferences influence and time information. Consequently, topics (semantically related probabilistic clusters of words) occurrence and correlations change over time, while the meaning of a particular topic almost remains unchanged. By using Bayes Theorem we can obtain topically related experts for different time periods and show how experts' interests and relationships change over time. Experimental results on scientific literature dataset show that the proposed generalized time topic modeling approach significantly outperformed the non-generalized time topic modeling approaches, due to simultaneously capturing conferences influence with time information.