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

Predicting menopausal symptoms with artificial neural network

“…Each input of the neuron is connected to one or several outputs of other similar neurons, thus forming a network. Modern neural network is a non-linear statistical data modeling tool to reflect the relationship between inputs and outputs, or to explore the data pattern (Chang, Chen, Chiu, & Chen, 2009;Hsieh, 2011;Hsu, 2011). Compared with conventional regression analysis, it has obvious advantages.…”

Developing an ontology-supported information integration and recommendation system for scholars

“…Content-based recommender systems base their recommendations on the similarity between new items, liked by users before, which described items with metadata and extracted features. Examples of content-based recommender systems are: Yuan and Cheng (Yuan & Cheng, 2004) presented a one-to-one recommendation mechanism that iteratively took inputs of the audio customer messages and produces personalized product analog structures deriving the generation of personalized heterogeneous products based on the coupled clustering algorithm; Blanco-Fernandez, Pazos-Arias, Gil-Solla, Ramos-Cabrer, and Lopez-Nores (2008) presented a personalization strategy that overcame the unresolved limitation of the existing recommender systems, i.e., overspecialization, by applying reasoning techniques borrowed from the semantic web; and Chang, Chen, Chiu, and Chen (2009) proposed an approach that trained the artificial neural networks to group users into different clusters and applied the well-established Kano's method to extracting the implicit needs from users in different clusters for improving information overloading in a real case of tour and travel websites.…”

Using refined Kano's model in Evaluating Kaohsiung Rapid Transit System service quality

“…Kano's model is also known as the theory of attractive quality and presents fi ve quality attributes or dimensions on the basis of the relationship between the degree of suff iciency of a given quality attribute in horizontal axis and customer satisfaction with that quality attribute in vertical axis to illustrate that product or service quality customer perceived and customer satisfaction are multi-dimensions. The philosophy of fi ve quality attributes is depicted as follows [1,4,9,11,14,24]:…”