Interaction-Based Recommendations for Online Communities

A key challenge in online communities is that of keeping a community active and alive. All online communities work hard to keep their members through various initiatives, such as personalisation and recommendation technologies. In online communities aimed at supporting behavioural change, that is, in domains such as diet, lifestyle, or the environment, the main reason for participation is not to connect with real-world friends for sharing and communicating, but to meet and gain support from like-minded people in an online environment. Introducing personalisation and recommendation features in these networks is challenging, as traditional approaches leverage the densely populated friendship relations found in typical social networks, and these are not present in these new community types. We address this challenge by looking beyond the articulated friendships of a community for evidence of relationships. In particular, we look at the interactions of members of an online community with other members and resources. In this article, we present a social behaviour model and apply it to two types of recommendation systems, a people recommender and a content recommender system. We evaluate our systems using the interaction logs of an online diet and lifestyle community in which 5,000 Australians participated in a 12-week programme. Our results show that our social behaviour-based recommendation algorithms outperform baselines, friendship-based, and link-prediction algorithms.