TY - GEN
T1 - Adaptive scoring method based on freshness for fresh information retrieval
AU - Uehara, Minoru
AU - Sato, Nobuyoshi
AU - Sakai, Yoshifumi
PY - 2005/12/1
Y1 - 2005/12/1
N2 - Fresh information is important for real business. In order to realize fresh information retrieval, we need not only to collect documents in a short time, but also to rank the results in the suitable order. However, conventional ranking methods are not suited for fresh information retrieval because they ignore temporal value of information. So, we have proposed the novel ranking method FTFIDF for fresh information retrieval. FTF·IDF extends TF·IDF by means of using FTF(fresh term frequency) instead of TF(term frequency). FTF differs from TF because FTF decreases as time goes. The speed of decreasing FTF is determined by the dumping factor. The dumping factor is sensitive against small changes of documents. So, we use a threshold to ignore such small changes. In some papers we published, we detect the optimal threshold manually. In this paper, we proposed an adaptive calculating method in order to detect threshold automatically. In this method, the optimal value is determined by iterating to test generated thresholds. In this paper, we describe our method and its evaluation.
AB - Fresh information is important for real business. In order to realize fresh information retrieval, we need not only to collect documents in a short time, but also to rank the results in the suitable order. However, conventional ranking methods are not suited for fresh information retrieval because they ignore temporal value of information. So, we have proposed the novel ranking method FTFIDF for fresh information retrieval. FTF·IDF extends TF·IDF by means of using FTF(fresh term frequency) instead of TF(term frequency). FTF differs from TF because FTF decreases as time goes. The speed of decreasing FTF is determined by the dumping factor. The dumping factor is sensitive against small changes of documents. So, we use a threshold to ignore such small changes. In some papers we published, we detect the optimal threshold manually. In this paper, we proposed an adaptive calculating method in order to detect threshold automatically. In this method, the optimal value is determined by iterating to test generated thresholds. In this paper, we describe our method and its evaluation.
KW - Distributed search engine
KW - Fresh information retrieval
KW - Ranking
UR - http://www.scopus.com/inward/record.url?scp=33845355192&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=33845355192&partnerID=8YFLogxK
U2 - 10.1109/WIRI.2005.5
DO - 10.1109/WIRI.2005.5
M3 - Conference contribution
AN - SCOPUS:33845355192
SN - 0769524141
SN - 9780769524146
T3 - Proceedings - International Workshop on Challenges in Web Information Retrieval and Integration, WIRI'05
SP - 226
EP - 231
BT - Proceedings - International Workshop on Challenges in Web Information Retrieval and Integration, WIRI'05
T2 - International Workshop on Challenges in Web Information Retrieval and Integration, WIRI'05
Y2 - 8 April 2005 through 9 April 2005
ER -