Context-aware recommendation of task allocations in service systems
In a service system comprising of knowledge intensive tasks, a pull-based allocation strategy (where knowledge workers decide on tasks to commit to, as opposed to having these commitments decided for them) can often be quite effective. Such a scenario is characterized by different types of tasks and workers with varying efficiencies. As workers and tasks change with time, a key challenge faced by knowledge workers is in deciding the most suitable tasks to commit to. Organizational roles of workers provide them the privilege of working on the tasks that the role is authorized to perform, but the suitability of a worker to perform a task varies because workers could have varying operational performance on different types of tasks. Past allocations, when correlated with execution histories annotated with quality of service (or performance) measures, can provide insights on the suitability of a task for a worker. It has been recognized that the effectiveness of a resource in performing a task often depends on the context in which the task is executed. In this work, we present a context-aware collaborative filtering recommender system that predicts a worker's suitability for a task, in different contexts or situations. The context-aware recommender uses information on the performance of similar resources in similar contexts to predict a resource's suitability for a task. Experiments performed on real-world execution logs demonstrate the effectiveness of the proposed approach.