Identifying User Destinations in VirtualWorlds

This paper focuses on the identification of human activity patterns in SecondLife (SL), a user-constructed virtual environment. SecondLife allows the users to create a virtual avatar, explore areas constructed by other users, socialize, and conduct financial transactions just as one would in the real world. However unlike the real world, new attractions can be constructed within hours and previous ones often fall into disuse rapidly. Without current information about the state of regions in the virtual world, it is difficult to infer the purpose of the user’s actions from location information. In this paper, we present an approach for gathering data on users’ activities and building a map of SecondLife annotated with information about activities that the users were able to perform in each region. Using this map, a recommender agent built into the user’s heads-up display can present suggestions of other areas to visit based on data collected from previous users. We discuss the the use of five supervised classifiers and report classification results for the map construction portion of the agent.