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Many tasks in data mining and related fields can be formalized as matchingbetween objects in two heterogeneous domains, including collaborativefiltering, link prediction, image tagging, and web search. Machine learningtechniques, referred to as learning-to-match in this paper, have beensuccessfully applied to the problems. Among them, a class of state-of-the-artmethods, named feature-based matrix factorization, formalize the task as anextension to matrix factorization by incorporating auxiliary features into themodel. Unfortunately, making those algorithms scale to real world problems ischallenging, and simple parallelization strategies fail due to the complexcross talking patterns between sub-tasks. In this paper, we tackle thischallenge with a novel parallel and efficient algorithm for feature-basedmatrix factorization. Our algorithm, based on coordinate descent, can easilyhandle hundreds of millions of instances and features on a single machine. Thekey recipe of this algorithm is an iterative relaxation of the objective tofacilitate parallel updates of parameters, with guaranteed convergence onminimizing the original objective function. Experimental results demonstratethat the proposed method is effective on a wide range of matching problems,with efficiency significantly improved upon the baselines while accuracyretained unchanged.

A Parallel and Efficient Algorithm for Learning to Match