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Multi-scenario learning (MSL) enables a service provider to cater for users'fine-grained demands by separating services for different user sectors, e.g.,by user's geographical region. Under each scenario there is a need to optimizemultiple task-specific targets e.g., click through rate and conversion rate,known as multi-task learning (MTL). Recent solutions for MSL and MTL are mostlybased on the multi-gate mixture-of-experts (MMoE) architecture. MMoE structureis typically static and its design requires domain-specific knowledge, makingit less effective in handling both MSL and MTL. In this paper, we propose anovel Automatic Expert Selection framework for Multi-scenario and Multi-tasksearch, named AESM^{2}. AESM^{2} integrates both MSL and MTL into a unifiedframework with an automatic structure learning. Specifically, AESM^{2} stacksmulti-task layers over multi-scenario layers. This hierarchical design enablesus to flexibly establish intrinsic connections between different scenarios, andat the same time also supports high-level feature extraction for differenttasks. At each multi-scenario/multi-task layer, a novel expert selectionalgorithm is proposed to automatically identify scenario-/task-specific andshared experts for each input. Experiments over two real-world large-scaledatasets demonstrate the effectiveness of AESM^{2} over a battery of strongbaselines. Online A/B test also shows substantial performance gain on multiplemetrics. Currently, AESM^{2} has been deployed online for serving majortraffic.

Automatic Expert Selection for Multi-Scenario and Multi-Task Search