Graph Structure Learning for Task Ordering

In many practical applications, multiple interrelated tasks must be accomplished sequentially through user interaction with retrieval, classification and recommendation systems. The ordering of the tasks may have a significant impact on the overall utility (or performance) of the systems; hence optimal ordering of tasks is desirable. However, manual specification of optimal ordering is often difficult when task dependencies are complex, and exhaustive search for the optimal order is computationally intractable when the number of tasks is large. We propose a novel approach to this problem by using a directed graph to represent partial-order preferences among task pairs, and using link analysis (HITS and Page Rank) over the graph as a heuristic to order tasks based on how important they are in reinforcing and propagating the ordering preference. These strategies allow us to find near-optimal solutions with efficient computation, scalable to large applications. We conducted a comparative evaluation of the proposed approach on a form-filling application involving a large collection of business proposals from the Accenture Consulting & Technology Company, using SVM classifiers to recommend keywords, collaborators, customers, technical categories and other related fillers for multiple fields in each proposal. With the proposed approach we obtained near-optimal task orders that improved the utility of the recommendation system by 27% in macro-averaged F1, and 13% in micro-averaged F1 compared to the results obtained using arbitrarily chosen orders, and that were competitive against the best order suggested by domain experts.