Fanjuan Shi

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Modern e-commerce websites are equipped with hybrid recommendation systems aiming at bringing novelty and diversity to consumers. However, mobilizing several recommendation algorithms simultaneously not only incurs unnecessary computation costs, but also jeopardizes consumers' shopping experience due to excessive information load. Hence, recommending less(More)
Consumers' online shopping behaviors are mostly determined by their intentions. Thus, the knowledge of consumer intention can help online marketers to enhance sales conversion rate and reduce ineffective marketing communications. Current personalization and recommendation techniques do not pay enough attention to various consumer intentions. The taxonomy of(More)
Current personalized recommendation approaches have reached a limit of effectiveness. By incorporating cognitive and behavioral knowledge, personalized recommender systems could be friendlier and more human-centric, which can potentially enhance user experience and loyalty. Our research proposes a psycho-cognitive method to recommend items based on users'(More)
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