COLT '92 Proceedings of the fifth annual workshop on Computational learning theory
Query expansion using local and global document analysis
SIGIR '96 Proceedings of the 19th annual international ACM SIGIR conference on Research and development in information retrieval
Wrappers for feature subset selection
Artificial Intelligence - Special issue on relevance
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Optimal aggregation algorithms for middleware
PODS '01 Proceedings of the twentieth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Unsupervised and supervised clustering for topic tracking
Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval
Hierarchical faceted metadata in site search interfaces
CHI '02 Extended Abstracts on Human Factors in Computing Systems
simVar: A Similarity-Influenced Question Selection Criterion for e-Sales Dialogs
Artificial Intelligence Review
Machine Learning
Faceted metadata for image search and browsing
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
COMPAQ QuickSource: Providing the Consumer with the Power of Artificial Intelligence
IAAI '93 Proceedings of the The Fifth Conference on Innovative Applications of Artificial Intelligence
Proceedings of the 17th International Conference on Data Engineering
Information Extraction: Techniques and Challenges
SCIE '97 International Summer School on Information Extraction: A Multidisciplinary Approach to an Emerging Information Technology
Evaluating Top-k Selection Queries
VLDB '99 Proceedings of the 25th International Conference on Very Large Data Bases
A Comparison ofModel-Based and Incremental Case-Based Approaches to Electronic FaultDiagnosis, (also in AAAI ''94 Workshop on CBR, D. Aha ed., Seattle, Washington, August 1994).
Feature Selection Methods for Conversational Recommender Systems
EEE '05 Proceedings of the 2005 IEEE International Conference on e-Technology, e-Commerce and e-Service (EEE'05) on e-Technology, e-Commerce and e-Service
iDistance: An adaptive B+-tree based indexing method for nearest neighbor search
ACM Transactions on Database Systems (TODS)
Automating the Design and Construction of Query Forms
ICDE '06 Proceedings of the 22nd International Conference on Data Engineering
Advances in conversational case-based reasoning
The Knowledge Engineering Review
Case-based recommender systems
The Knowledge Engineering Review
Proceedings of the 2006 ACM SIGMOD international conference on Management of data
Simple questions to improve pseudo-relevance feedback results
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
A Survey of Web Information Extraction Systems
IEEE Transactions on Knowledge and Data Engineering
Nearest-Neighbor Methods in Learning and Vision: Theory and Practice (Neural Information Processing)
Nearest-Neighbor Methods in Learning and Vision: Theory and Practice (Neural Information Processing)
Information and Management
Towards keyword-driven analytical processing
Proceedings of the 2007 ACM SIGMOD international conference on Management of data
Shooting stars in the sky: an online algorithm for skyline queries
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
Probabilistic ranking of database query results
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
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Helpdesk databases are used to store past interactions between customers and companies to improve customer service quality. One common scenario of using helpdesk database is to find whether recommendations exist given a new problem from a customer. However, customers often provide incomplete or even inaccurate information. Manually preparing a list of clarification questions does not work for large databases. This paper investigates the problem of automatic generation of a minimal number of questions to reach an appropriate recommendation. This paper proposes a novel dynamic active probing method. Compared to other alternatives such as decision tree and case-based reasoning, this method has two distinctive features. First, it actively probe the customer to get useful information to reach the recommendation, and the information provided by customer will be immediately used by the method to dynamically generate the next questions to probe. This feature ensures that all available information from the customer is used. Second, this method is based on a probabilistic model, and uses a data augmentation method which avoids overfitting when estimating the probabilities in the model. This feature ensures that the method is robust to databases that are incomplete or contain errors. Experimental results verify the effectiveness of our approach.