A Hybrid Recommendation Approach for One and Only Items
Many mechanisms have been developed to deliver only relevant informationto the web users and prevent information overload. The most popularrecent developments in the e-cornmerce domain are the user-preference basedpersonalization and recommendation techniques. However, the existing techniqueshave a major drawback - poor accuracy of recommendation on one-andonlyitems - because most of them do not understand the item's semantic featuresand attributes. Thus, in this study, we propose a novel Semantic ProductRelevance model and its attendant personalized recommendation approach toassist Export business selecting the right international trade exhibitions for warketpromotion. A recommender system, called Smart Trade Exhibition Finder(STEF), is developed to tailor the relevant trade exhibition information to eachparticular business user. STEF reduces significantly the time, cost and riskfaced by exporters in selecting, entering and developing international markets.In particular, the proposed model can be used to overcome the drawback of existingrecommendation techniques.
Year of publication: |
2005
|
---|---|
Authors: | Guo Xuetao ; Zhang Guangquan ; Chew Eng ; Burdon Stephen |
Other Persons: | Zhang, S (contributor) ; Jarvis, R (contributor) |
Publisher: |
Springer-Verlag |
Saved in:
freely available
Saved in favorites
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