New online personalized recommendation approach based on the perceived value of consumer characteristics

WEB 2.0 facilitates the bidirectional communication capabilities of online review, causing the personalization and asymmetry of online review. Despite the problem of online personalized recommendation system, the influence of consumer characteristics on consumer repurchase intention is insufficiently examined in the extant literature. To address this issue, this study proposes a new online personalized recommendation approach based on the perceived value of consumer characteristics. Two aspects of the proposed framework are addressed. The first aspect is the linguistic information transformation model, which converts online reviews to unbalanced linguistic label cloud. The second aspect is an online recommendation approach based on the linguistic information trans-formation model. A series of experiments are conducted based on a set of hotel assessment data from four cities and the electronic consumer record of four consumers selected randomly. Results show that the proposed cluster method is useful for identifying consumer characteristics and gives personalized recommendation. Overall, this method reduces computation and provides a reference point based on consumer characteristics.