A proactive personalised mobile recommendation system using analytic hierarchy process and Bayesian network

Abstract

Abstract With the growth, ready availability and affordability of wireless technologies, proactive context-aware recommendations are a potential solution to overcome the information overload and the common limitations of mobile devices (inconvenience of data input and Internet browsing). The automatic provision of just-in-time information or recommendations tailored to each user’s needs/preferences contextualised from the user’s activities, location, usage patterns, time, and connectivity may not only facilitate access to information but also remove barriers to the adoption of current and future services on mobile devices. This paper describes a hybrid P2P context-aware framework called JHPeer which supports a variety of context-aware applications in mobile environments. Any context-aware information services such as recommendation services could use the collected and shared contextual information in JHPeer network. An analytic hierarchy process based multi-criteria ranking (AHP-MCR) approach has been developed and used to rate recommendations in a variety of domains. The weights of the contexts criteria can be assigned by the user or automatically adjusted via individual-based and/or group-based assignment. Additionally, a Bayesian network algorithm is applied to solve the cold-start problem in recommendation systems. The paper also proposes a strategy for using Bayesian networks for recommendation services. A news recommendation application has been implemented on the developed JHPeer framework, which proactively pushes relevant news based on the users’ contextual information. Evaluation studies show that the system can push relevant recommendations to mobile users appropriately.