Come Along

In this paper we study the factors that affect people’s decision in participating in leisure activities in the social and cultural environment. To this end, we collected the ratings of local people from three different cities around the world on standard leisure activities, and looked at the personal, social and contextual features shaping their preferences. We then used this dataset to evaluate how these features can be exploited to recommend places people would actually like. Our initial results suggest that friends are a good source for recommending places, with higher precision and recall than considering only popular places, but these can be improved reducing the scope to similar friends in the context of the particular activity. We have also found that people preferences are sensitive to the companion (e.g., partner, friends, tourists) for which they look for different features. The results also suggest that similarities in the preferences of people can be extended to other activities, which points to the potential of profiling users based on lifestyle. We finally present the design and prototype of a system, namely Come Along, which aims at helping people discover, find and participate to social and leisure activities.

In this paper we present a social platform that empowers real-life social experiences by using the crowd to help people discover interesting events around a particular location and make the best of the actual experience. We demonstrate how current systems cover only partially the social and individual motivations on deciding and teaming up for events, and propose i) a system of implicit and explicit incentives that rewards intention and experience sharing within circles based on friendship or interests and ii) a discovery algorithm that combines the importance of coming along with friends with the personal interests to recommend events. The resulting platform has been applied to generic events as well as domain-specific ones such as shows in movie theaters.