Abstract
Usefulness recognition model of consumer online reviews can filter out useless ones in all reviews, which can reduce cost of searching for commodity information for potential consumers. However, existing recognition models were based on that all consumers judge usefulness of online reviews by same criteria, ignoring differences of theirs. Thus, a new transfer learning support vector machine model was proposed in this paper to realize individualized recognition of online reviews usefulness. Theoretical analysis and experimental results showed that proposed algorithm could achieve better performance with less training data.
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