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Collaborative Filtering - A Group Profiling Algorithm for Personalisation in a Spatial Recommender System

As the quantity of geospatial information rapidly increases, information overload in the spatial domain is becoming a serious issue. Often the amount of information being displayed on digital maps makes it difficult to determine useful content. In order to assist in resolving this problem, personalisation techniques have been developed. The most effective techniques implicitly monitor user interactions with map interfaces and content in order to infer user interests. This permits the map to be adapted to suit individual users by highlighting or displaying a subset of available content. The work presented in this paper builds on this paradigm to refine map adaptations by using group profiling techniques. Such techniques identify similar users to the target user and utilise their interests as an indicator of possible interests for the target user. The map can then be adapted accordingly. The approach has been effective in the non-spatial domain however it has not been widely studied in the spatial context. The methodology behind this technique is presented in this paper while the approach is demonstrated through a case study of a map navigational assistant.