DeCAT 2015 - Workshop on Deep Content Analytics Techniques for Personalized and Intelligent Services

Personal Information Management (PIM) research is challenging primarily due to the inherent nature of PIM. Studies have shown that people often adopt their own schemes when organising their personal collections, possibly because PIM tool-support is still lacking. In this paper we investigate the problem of automatic organisation of personal information into task-clusters by transparently exploiting the user’s behaviour while performing some tasks. We conduct a controlled experiment, with 22 participants, using three different task-execution strategies to gather clean data for our evaluation. We use our PiMx (PIM analytix) framework to analyse this data and understand better the issues associated with this problem. Based on this analysis, we then present the incremental density-based clustering algorithm, iDeTaCt, that is able to transparently generate task-clusters by exploiting document switching and revisitation. We evaluate the algorithm’s performance using the collected datasets. The results obtained are very encouraging and merit further investigation.

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