Application of neural networks and Kano’s method to content recommendation in web personalization

As customers become more skilled in the use of internet, many companies have gradually established their websites with more and more enormous information to get future competition in electronic commerce (EC). However, the miscellaneous information often brings the users at a loss. Web personalization provides a solution to improvement of information overloading on websites. The objective of web personalization is to give users a website they want or need, and thus knowing the needs of users is an important task for content recommendation in web personalization. In this article, we propose a hybrid approach for this task. The proposed approach trains the artificial neural networks to group users into different clusters, and applies the well-established Kano’s method to extracting the implicit needs from users in different clusters. Finally, a real case of tour and travel websites applying the approach is presented to demonstrate the improvement of information overloading.