An adaptive fuzzy Information Retrieval model to improve response time perceived by e-commerce clients

dc.contributor.authorAjayi, Anuoluwapo Olanrewaju
dc.contributor.authorAderounmu, Ganiyu A.
dc.contributor.authorSoriyan, H.A.
dc.date.accessioned2023-05-13T17:53:41Z
dc.date.available2023-05-13T17:53:41Z
dc.date.issued2010-01
dc.description37(1):82–91en_US
dc.description.abstractIn this paper, an adaptive fuzzy logic-based information retrieval model is presented to enable users retrieve exact and specific information they sort after. The proposed IR model takes into consideration the limited bandwidth between ISP and its users; and the characteristics (processor speed, memory size, resolution, availability of anti-virus, etc.) of clients’ devices in ensuring that a customer has a fruitful and eventful session while conducting business online. The model was designed using unified modelling language and implemented using Borland JBuilder. A performance evaluation of the proposed information retrieval system using two evaluation measures was conducted. The experimental result indicated that the model has an acceptable performance.en_US
dc.identifier.citationAjayi A.O, Aderounmu G.A, Soriyan H.A.(2010)An adaptive fuzzy Information Retrieval model to improve response time perceived by e-commerce clients. Expert Systems with Applicationsen_US
dc.identifier.otherDOI: 10.1016/j.eswa.2009.05.071
dc.identifier.urihttps://ir.oauife.edu.ng/123456789/5529
dc.language.isoenen_US
dc.subjectadaptive fuzzyen_US
dc.subjectlogic-baseden_US
dc.subjectinformationen_US
dc.subjectretrieval modelen_US
dc.subjectresponse timeen_US
dc.subjecte-commerce clientsen_US
dc.titleAn adaptive fuzzy Information Retrieval model to improve response time perceived by e-commerce clientsen_US
dc.typeJournalen_US
Files
Original bundle
Now showing 1 - 1 of 1
Loading...
Thumbnail Image
Name:
citation-257403350.txt
Size:
1008 B
Format:
Plain Text
Description:
Journal Article
License bundle
Now showing 1 - 1 of 1
Loading...
Thumbnail Image
Name:
license.txt
Size:
1.71 KB
Format:
Item-specific license agreed upon to submission
Description:
Collections