Improving the prediction accuracy of recommendation algorithms: Approaches anchored on human factors

Recommender systems are a special class of personalized systems that aim at predicting a user's interest on available products and services by relying on previously rated items or item features. Human factors associated with a user's personality or lifestyle, although potential determinants of user behavior are rarely considered in the personalization process. In this paper, we demonstrate how the concept of lifestyle can be incorporated in the recommendation process to improve the prediction accuracy by efficiently managing the problem of limited data availability. We propose two approaches: one relying on lifestyle alone and another integrating lifestyle within the nearest neighbor approach. Both approaches are empirically tested in the domain of recommendations for personalized television advertisements and are shown to outperform existing nearest neighborhood approaches in most cases.