Detection of shifts in user interests for personalized information filtering
SIGIR '96 Proceedings of the 19th annual international ACM SIGIR conference on Research and development in information retrieval
Fab: content-based, collaborative recommendation
Communications of the ACM
Methods and metrics for cold-start recommendations
SIGIR '02 Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval
Hybrid Recommender Systems: Survey and Experiments
User Modeling and User-Adapted Interaction
Implicit feedback for inferring user preference: a bibliography
ACM SIGIR Forum
Location Based Services and TeleCartography (Lecture Notes in Geoinformation and Cartography)
Location Based Services and TeleCartography (Lecture Notes in Geoinformation and Cartography)
Personalised maps in multimodal mobile GIS
International Journal of Web Engineering and Technology
An Adaptive User Profile Based on Memory Model
WAIM '08 Proceedings of the 2008 The Ninth International Conference on Web-Age Information Management
Location Based Services and TeleCartography II: From Sensor Fusion to Context Models
Location Based Services and TeleCartography II: From Sensor Fusion to Context Models
Understanding geospatial interests by visualizing map interaction behavior
Information Visualization
RecoMap: an interactive and adaptive map-based recommender
Proceedings of the 2010 ACM Symposium on Applied Computing
Collaborative Spatial Object Recommendation in Location Based Services
ICPPW '10 Proceedings of the 2010 39th International Conference on Parallel Processing Workshops
A hybrid approach for spatial web personalization
W2GIS'05 Proceedings of the 5th international conference on Web and Wireless Geographical Information Systems
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Fuelled by the quantity of available online spatial data that continues to grow, the requirement for filtering spatial content to match mobile users' context becomes increasingly important. This paper introduces a flexible algorithm to derive users' preferences in a mobile and distributed system. Such preferences are implicitly computed from users' virtual and physical interactions with spatial features. Using this concept, region profiles for specific spatial contexts can be generated and used to recommend content to those visiting that region. Our approach provides a set of profiles (personal and region-based) which are combined to adapt the presentation of a given service to suit users' immediate needs and interests. A proposed college campus navigation assistant illustrates the benefits of such an unobtrusive recommender system.