Predicting user attitudes toward smartphone ads using support vector machine
by Kang Woo Lee; Hyunseung Choo
International Journal of Mobile Communications (IJMC), Vol. 14, No. 3, 2016

Abstract: This study presents a computational model of smartphone ads that uses support vector machine (SVM). The model is used to simulate the well-known social phenomenon of 'similarity attraction,' which we analysed using both regression and pattern classification models. Smartphone call patterns were used to predict user personality for the given smartphone call patterns and ad types (extrovert or introvert), the model simulated the similarity attraction effect and predicted user attitudes toward the smartphone ad in terms of likeability, credibility and buying intention. The results indicated that the SVM model is a powerful tool for simulating similarity attraction and correctly classifies user attitude. The computational implication of the model is discussed in terms of customisation and persuasiveness.

Online publication date: Sat, 30-Apr-2016

The full text of this article is only available to individual subscribers or to users at subscribing institutions.

Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.

Pay per view:
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.

Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Mobile Communications (IJMC):
Login with your Inderscience username and password:

    Username:        Password:         

Forgotten your password?

Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.

If you still need assistance, please email