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In this paper, we focus on learning effective entity matching models overmulti-source large-scale data. For real applications, we relax typicalassumptions that data distributions/spaces, or entity identities are sharedbetween sources, and propose a Relaxed Multi-source Large-scale Entity-matching(RMLE) problem. Challenges of the problem include 1) how to align large-scaleentities between sources to share information and 2) how to mitigate negativetransfer from joint learning multi-source data. What's worse, one practicalissue is the entanglement between both challenges. Specifically, incorrectalignments may increase negative transfer; while mitigating negative transferfor one source may result in poorly learned representations for other sourcesand then decrease alignment accuracy. To handle the entangled challenges, wepoint out that the key is to optimize information sharing first based on Paretofront optimization, by showing that information sharing significantlyinfluences the Pareto front which depicts lower bounds of negative transfer.Consequently, we proposed an Incentive Compatible Pareto Alignment (ICPA)method to first optimize cross-source alignments based on Pareto frontoptimization, then mitigate negative transfer constrained on the optimizedalignments. This mechanism renders each source can learn based on its truepreference without worrying about deteriorating representations of othersources. Specifically, the Pareto front optimization encourages minimizinglower bounds of negative transfer, which optimizes whether and which to align.Comprehensive empirical evaluation results on four large-scale datasets areprovided to demonstrate the effectiveness and superiority of ICPA. Online A/Btest results at a search advertising platform also demonstrate theeffectiveness of ICPA in production environments.

Incentive Compatible Pareto Alignment for Multi-Source Large Graphs