Personalized Attraction Enhanced Sponsored Search with Multi-task Learning
| Autor: | Ziyu Guan, Hongmin Liu, Wanxian Guan, Jiming Chen, Beidou Wang, Wei Zhao, Wei Ning, Guang Qiu, Boxuan Zhang |
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| Jazyk: | angličtina |
| Rok vydání: | 2019 |
| Předmět: |
FOS: Computer and information sciences
Point (typography) Computer science business.industry Multi-task learning 02 engineering and technology E-commerce Space (commercial competition) Preference Personalization Computer Science - Information Retrieval Product (business) World Wide Web User experience design 020204 information systems 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing business Information Retrieval (cs.IR) |
| Zdroj: | KDD |
| Popis: | 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%. |
| Databáze: | OpenAIRE |
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