A group recommendation system with consideration of interactions among group members

As on-line community activities have increased exponentially, the need for a group recommendation system has also become more and more imperative. Although, the traditional recommendation system has achieved great success in supporting individuals’ purchasing decisions, it is not suitable for supporting group purchasing decisions because its input can neither include items’ ratings given by groups, nor can it generate recommendations for groups. Therefore, this study proposes a novel group recommendation system to satisfy this demand. The system is designed based on the framework of collaborative filtering. Especially, we use genetic algorithm to predict the possible interactions among group members so that we can correctly estimate the rating that a group of members might give to an item. The experimental results show that the proposed system can give satisfactory and high quality group recommendations.