Measuring the Variation in Task-Needs for Knowledge Delivery: A Profiling via Collaboration Technique

Effective knowledge management (KM) in a knowledge-intensive working environment requires an understanding of workers´ information needs for tasks, (task-needs), so that they can be provided with appropriate codified knowledge (textual documents) when performing long-term tasks. This work proposes a novel profiling technique based on implicit relevance feedback and collaborative filtering techniques that model workers´ task-needs. The proposed profiling method analyses variations in workers´ task-needs for topics (i.e., topic needs) in a topic taxonomy over time. Variations in the topic needs of similar workers´ are used to predict variations in a target worker´s topic needs and adjust his/her task profile accordingly. Experiment results suggest that considering variations in the topic needs of similar workers´ during the profile adaptation process is effective in improving the precision of document retrieval.