A Recommendation Model Using the Bandwagon Effect for E-Marketing Purposes in IoT

• Published in: International journal of distributed sensor networks Vol. 2015; no. 7; p. 475163
• Main Authors: Choi, Sang-Min, Lee, Hyein, Han, Yo-Sub, Man, Ka Lok, Chong, Woon Kian
• Format: Journal Article
• Language: English
• Published: London, England Hindawi Publishing Corporation 01.01.2015; SAGE Publications; Sage Publications Ltd. (UK); Sage Publications Ltd; Wiley
• Subjects: Construction; Education; Engineering research; Internet; Internet marketing; Internet of Things; Methods; Networks; Personalized; Platforms; Recommendations; Recommender systems
• ISSN: 1550-1329; 1550-1477; 1550-1477
• DOI: 10.1155/2015/475163

Recently, the Internet of things (IoT) became useful in various applications based on the web communication technology. The IoT has great potential in several service domains including cultural, educational, or medical areas. We consider a recommendation technique suitable for the IoT-based service. A personalized recommender system often relies on user preferences for better suggestions. We notice that we need a different recommendation approach in the IoT platform. While the conventional recommendation approaches rely on user preferences provided by users, these approaches may not be suitable for the IoT environment. The conventional systems utilize user ratings for items to compose recommendation list. This implies that the systems require additional user activities such as adding their preferences. We notice that the IoT environment can naturally provide user information such as users’ item selection history without users’ additional actions. We propose a recommendation model that does not require users’ additional actions and is more suitable for the IoT environment. We examine the usability of the bandwagon effect to build a new recommender system based on users’ selection history. We first consider the bandwagon effects in movie recommendation domain and show its usefulness for the IoT. We then suggest how to use the bandwagon effect in recommender systems with IoT.