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
Corpus structure, language models, and ad hoc information retrieval
Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval
Improving web search results using affinity graph
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
Respect my authority!: HITS without hyperlinks, utilizing cluster-based language models
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
Document re-ranking using cluster validation and label propagation
CIKM '06 Proceedings of the 15th ACM international conference on Information and knowledge management
Language model information retrieval with document expansion
HLT-NAACL '06 Proceedings of the main conference on Human Language Technology Conference of the North American Chapter of the Association of Computational Linguistics
Multimedia search reranking: A literature survey
ACM Computing Surveys (CSUR)
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Re-ranking (RR) and Cluster-based Retrieval (CR) have been polar methods for improving retrieval effectiveness by using inter-document similarities. However, RR and CR improve precision and recall respectively, not simultaneously. Thus, the improvement through RR and CR may be different according to whether a query is recall-deficient or not. However, previous researchers missed out this point, and separately investigated individual approaches, causing a limited improvement. To reflect all of positive effects by RR and CR, this paper proposes RCR, the re-ranking with cluster-based retrieval where RR is applied to initially-retrieved results of CR. Experimental results show that RCR significantly improves the baseline, while CR or RR sometimes does not significantly improve the baseline.