Recommendation Systems (9): Multi-Task Learning and Multi-Objective Optimization

Thu, 25 Dec 2025 09:00:00 +0000

A live e-commerce ranker doesn’t optimize just one number. The same model that decides which product to show you also predicts, in the same forward pass, whether you will click, add it to your cart, pay for it, return it, or leave a positive review. Each prediction is a different task with its own data distribution, scarcity, and incentives. These tasks are tightly coupled: a clicker is more likely to convert, a converter is more likely to write a review, and a high-CTR thumbnail can attract clicks that reduce watch time.

Transfer Learning (6): Multi-Task Learning

Sat, 31 May 2025 09:00:00 +0000

A self-driving car using a single camera needs to do three things simultaneously: detect cars and pedestrians, segment lanes and free space, and estimate the distance of each pixel. Training three separate networks would triple the parameters, require three times as many forward passes at inference, and overlook the fact that all three tasks need the same low-level features (edges, surfaces, occlusion cues).

Multi-task learning (MTL) is the alternative: one shared backbone, one task-specific head per output, all trained jointly. Done well, you cut parameters by 60% and lift accuracy on every task because each task acts as a regularizer for the others. Done badly, two of your three tasks regress and you waste a week wondering why.

Multi-Task Learning on Chen Kai Blog

Recommendation Systems (9): Multi-Task Learning and Multi-Objective Optimization

Transfer Learning (6): Multi-Task Learning