Mukhopadhyay, S.; Peng, S.; Raje, R.; Mostafa, J.; Palakal, M.: Distributed multi-agent information filtering : a comparative study (2005)
0.01
0.0064184438 = product of:
0.01925533 = sum of:
0.01925533 = product of:
0.03851066 = sum of:
0.03851066 = weight(_text_:management in 3559) [ClassicSimilarity], result of:
0.03851066 = score(doc=3559,freq=2.0), product of:
0.17235184 = queryWeight, product of:
3.3706124 = idf(docFreq=4130, maxDocs=44218)
0.051133685 = queryNorm
0.22344214 = fieldWeight in 3559, product of:
1.4142135 = tf(freq=2.0), with freq of:
2.0 = termFreq=2.0
3.3706124 = idf(docFreq=4130, maxDocs=44218)
0.046875 = fieldNorm(doc=3559)
0.5 = coord(1/2)
0.33333334 = coord(1/3)
- Abstract
- Information filtering is a technique to identify, in large collections, information that is relevant according to some criteria (e.g., a user's personal interests, or a research project objective). As such, it is a key technology for providing efficient user services in any large-scale information infrastructure, e.g., digital libraries. To provide large-scale Information filtering services, both computational and knowledge management issues need to be addressed. A centralized (single-agent) approach to information filtering suffers from serious drawbacks in terms of speed, accuracy, and economic considerations, and becomes unrealistic even for medium-scale applications. In this article, we discuss two distributed (multiagent) information filtering approaches, that are distributed with respect to knowledge or functionality, to overcome the limitations of single-agent centralized information filtering. Large-scale experimental studies involving the weIl-known TREC data set are also presented to illustrate the advantages of distributed filtering as weIl as to compare the different distributed approaches.
