Paper Info
Title
Quality biased thread retrieval using the voting model.
Abstract
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.
Year
DOI
Venue
2013
10.1145/2537734.2537752
ADCS
Keywords
Field
DocType
content quality,quality indicator,thread level feature,message content quality,forum content quality,voting model,high quality content,ranking thread,thread retrieval,message level quality feature,relevant thread,message quality indicator,information retrieval,information quality
Learning to rank,Data mining,Voting,Information retrieval,Ranking,Computer science,Thread (computing),Information quality
Conference
Citations 
PageRank 
References 
0
0.34
10
Authors
2
Name
Order
Citations
PageRank
Ameer Tawfik Albaham1273.36
Naomie Salim242448.23