Clustering for personalized mobile Web usage

Web access from mobile devices presents its own unique challenges because of severe resource constraints on the mobile devices (power, form factor, bandwidth, etc.). Hence, instead of reacting to a user´s requests, it would be better to try and predict a user´s actions. This would allow time for the server (on the fixed-wired side) to pre-fetch data and pre-process it into a wireless-friendly format (such as the PQA format required by Palm Pilots). Information that flows on the wireless link should be tailored to match what the user wants (rather than making the user wander through Web, which wastes bandwidth and increases latency experienced by the user). Adaptive user clustering and profiling is essential to be able to accurately predict user actions. In this paper we present results of our clustering and personalization project. We compare several distance measures used in clustering. We introduce a new measure to assess the quality of clustering independent of the distance measure used in the clustering-algorithm. We also compare different strategies