A novel recommendation model of location-based advertising: Context-Aware Collaborative Filtering using GA approach

Recommender systems are the efficient and most used tools that prevail over the information overload problem, provide users with the most appropriate content by considering their personal preferences (mostly, ratings). In addition to these preferences, taking into account the interaction context of users will improve the relevancy of the recommendation process. However, only a few prior studies have tried to adopt context-awareness to the recommendation model. Although a number of studies have developed recommendation models using collaborative filtering (CF), few of them have tried to adopt both CF and other artificial intelligence techniques, such as genetic algorithm (GA), as a tool to improve recommendation results. In this paper, we propose a new recommendation model, which we termed Context-Aware Collaborative Filtering using genetic algorithm (CACF-GA), for location-based advertising (LBA) based on both user’s preferences and interaction’s context. We first defined discrete contexts, and then applied the concept of “context similarity” to conventional CF to create the context-aware recommendation model. The context similarity between two contexts is designed to be optimized using GA. We collect real-world data from mobile users, build a LBA recommendation model using CACF-GA, and then perform an empirical test to validate the usefulness of CACF-GA. Experiments show our proposed model provides the most accurate prediction results compared to comparative ones.