Term-weighting approaches in automatic text retrieval
Information Processing and Management: an International Journal
A re-examination of text categorization methods
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval
Modern Information Retrieval
A Probabilistic Analysis of the Rocchio Algorithm with TFIDF for Text Categorization
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
Combining document representations for known-item search
Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval
Graph-based ranking algorithms for e-mail expertise analysis
DMKD '03 Proceedings of the 8th ACM SIGMOD workshop on Research issues in data mining and knowledge discovery
Expertise identification using email communications
CIKM '03 Proceedings of the twelfth international conference on Information and knowledge management
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
Voting for candidates: adapting data fusion techniques for an expert search task
CIKM '06 Proceedings of the 15th ACM international conference on Information and knowledge management
Probabilistic models for expert finding
ECIR'07 Proceedings of the 29th European conference on IR research
Suggesting friends using the implicit social graph
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
ACM Transactions on Information Systems (TOIS)
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
Machine Learning
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Finding persons who are knowledgeable on a given topic (i.e. Expert Search) has become an active area of recent research [1, 2, 3]. In this paper we investigate the related task of Intelligent Message Addressing, i.e., finding persons who are potential recipients of a message under composition given its current contents, its previously-specified recipients or a few initial letters of the intended recipient contact (intelligent auto-completion). We begin by providing quantitative evidence, from a very large corpus, of how frequently email users are subject to message addressing problems. We then propose several techniques for this task, including adaptations of well-known formal models of Expert Search. Surprisingly, a simple model based on the K-Nearest-Neighbors algorithm consistently outperformed all other methods. We also investigated combinations of the proposed methods using fusion techniques, which leaded to significant performance improvements over the baselines models. In auto-completion experiments, the proposed models also outperformed all standard baselines. Overall, the proposed techniques showed ranking performance of more than 0.5 in MRR over 5202 queries from 36 different email users, suggesting intelligent message addressing can be a welcome addition to email.