Context-Based Personalization for Mobile Web Search

User experience while searching for web pages on the move can be far from satisfactory due to the inherent limitations of the input modes available in mobile devices. On the other hand, end-users can benefit from the availability of context-aware information anywhere, anytime.

To overcome the usability problem and exploit context information at the same time, we propose a thesaurus-based semantic context-aware autocompletion mechanism. Our system can help the user in completing the desired query terms avoiding manual typing.

In addition we are capable of filtering out on-relevant query terms for the Context in which the search process is conducted. Our context-aware proposal is based on a model which represents formally all the information about the user circumstances, the access mechanism (device and web browser) and the surrounding environment. Our evaluation reveals that users can nd new relevant context-aware results with less effort.
Source: Stanford University
Author: Mario Arias | Jose M. Cantera | Jesus Vegas

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