Intention-Based Online Consumer Classification for Recommendation and Personalization. Hot Topics in Web Systems and Technologies
Résumé
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 online shopping intention and the method to predict intention in real time are yet to be developed. Based on unsupervised and supervised learning techniques, this paper proposes an intention prediction model to fulfill the research gap. Empirical results suggest that the proposed model is able to classify intentions precisely. Accordingly, we discuss the implication and provide some managerial suggestions to online marketers who seek to implement some intention-based personalization methods.
Mots clés
Internet
consumer behaviour
unsupervised learning
intention-based online consumer classification
online marketers
online shopping behaviors
supervised learning techniques
unsupervised learning techniques
Classification algorithms
Machine learning algorithms
Prediction algorithms
Real-time systems
Taxonomy
Web pages
classification
clustering
consumer intention
data mining
e-commerce
machine learning