User profiling for the Melvil knowledge retrieval system

Melvil is an ontology-based knowledge retrieval platform that provides a three-dimensional visualization of search results. The user can tailor the presentation of the search results to his or her preferences by changing the settings of various parameters on the screen. In this paper, we report on a prototype implementation of a user profiling device that learns to predict appropriate settings for these parameters for the current search results based on previous experiences. In a preliminary study, we evaluated several off-the-shelf machine learning algorithms on parts of the problem. The final implementation required the flexibility of handling both regression and classification problems, being able to deal with set-valued input and output attributes, as well as incorporating Melvil's ontologies for the respective application domain. Thus, we selected a nearest-neighbor approach for the prototype implementation. An evaluation on off-line data collected from several users showed a satisfactory performance.