Probabilistic models of information retrieval based on measuring the divergence from randomness
ACM Transactions on Information Systems (TOIS)
Relevance weighting for query independent evidence
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
Linear feature-based models for information retrieval
Information Retrieval
Voting techniques for expert search
Knowledge and Information Systems
Key blog distillation: ranking aggregates
Proceedings of the 17th ACM conference on Information and knowledge management
It pays to be picky: an evaluation of thread retrieval in online forums
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
Learning models for ranking aggregates
ECIR'11 Proceedings of the 33rd European conference on Advances in information retrieval
Online community search using conversational structures
Information Retrieval
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Thread retrieval is an essential tool in knowledge-based forums. However, forum content quality varies from excellent to mediocre and spam; thus, search methods should find not only relevant threads but also those with high quality content. Some studies have shown that leveraging quality indicators improves thread search. However, these studies ignored the hierarchical and the conversational structures of threads in estimating topical relevance and content quality. In that regard, this paper introduces leveraging message quality indicators in ranking threads. To achieve this, we first use the Voting Model to convert message level quality features into thread level features. We then train a learning to rank method to combine these thread level features. Preliminary results with some features reveal that representing threads as collections of messages is superior to treating them as concatenations of their messages. The results show also the utility of leveraging message content quality as compared to non quality-based methods.