TimedTextRank: adding the temporal dimension to multi-document summarization
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
Link analysis using time series of web graphs
Proceedings of the sixteenth ACM conference on Conference on information and knowledge management
AMAZING: A sentiment mining and retrieval system
Expert Systems with Applications: An International Journal
COLING '08 Proceedings of the 22nd International Conference on Computational Linguistics - Volume 1
Time-weighted web authoritative ranking
Information Retrieval
Page importance computation based on Markov processes
Information Retrieval
Information Sciences: an International Journal
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The most well known search techniques are perhaps the PageRank and HITS algorithms. In this paper we argue that these algorithms miss an important dimension, the temporal dimension. Quality pages in the past may not be quality pages now or in the future. These techniques favor older pages because these pages have many in-links accumulated over time. New pages, which may be of high quality, have few or no in-links and are left behind. Research publication search has the same problem. If we use the PageRank or HITS algorithm, those older or classic papers will be ranked high due to the large number of citations that they received in the past. This paper studies the temporal dimension of search in the context of research publication. A number of methods are proposed to deal with the problem based on analyzing the behavior history and the source of each publication. These methods are evaluated empirically. Our results show that they are highly effective.