Personalized Attraction Enhanced Sponsored Search with Multi-task Learning
This addresses the challenge of improving ad click-through rates and revenue for e-commerce platforms like Taobao, while maintaining user experience, though it is incremental in applying multi-task learning to a specific domain.
The paper tackles the problem of enhancing sponsored search in e-commerce by personalizing ad titles with selling point keywords to attract users, particularly in mobile settings, resulting in a 2% increase in revenue per thousand impressions and a merchant opt-out rate below 4%.
We study a novel problem of sponsored search (SS) for E-Commerce platforms: how we can attract query users to click product advertisements (ads) by presenting them features of products that attract them. This not only benefits merchants and the platform, but also improves user experience. The problem is challenging due to the following reasons: (1) We need to carefully manipulate the ad content without affecting user search experience. (2) It is difficult to obtain users' explicit feedback of their preference in product features. (3) Nowadays, a great portion of the search traffic in E-Commerce platforms is from their mobile apps (e.g., nearly 90% in Taobao). The situation would get worse in the mobile setting due to limited space. We are focused on the mobile setting and propose to manipulate ad titles by adding a few selling point keywords (SPs) to attract query users. We model it as a personalized attractive SP prediction problem and carry out both large-scale offline evaluation and online A/B tests in Taobao. The contributions include: (1) We explore various exhibition schemes of SPs. (2) We propose a surrogate of user explicit feedback for SP preference. (3) We also explore multi-task learning and various additional features to boost the performance. A variant of our best model has already been deployed in Taobao, leading to a 2% increase in revenue per thousand impressions and an opt-out rate of merchants less than 4%.