Recommendation Algorighm Engineer - E-Commerce

Team Introduction:
TikTok E-commerce is a content e-commerce business based on TikTok short-video products. Committed to becoming users' preferred platform for discovering and acquiring high-quality products at favorable prices, in scenarios like live-stream e-commerce and video content e-commerce, the TikTok E-commerce business aims to provide users with more personalized, proactive, and efficient consumption experiences, offer merchants stable and reliable platform services, fulfill the mission of making high-quality products easy to sell in more regions and bringing a better life within reach.

We invite you to grow, delve deep, and unleash unlimited potential here, together tackling challenges in technology and business. The team currently has rich experience in international product R&D, embraces diverse cultures, and has established R&D teams globally. Join us to take on the challenge of cross-border collaboration, with business trip and overseas assignment opportunities waiting for you!

Research Project Introduction:
As the world's leading short-video platform, TikTok faces multiple challenges in its recommendation systems, including data sparsity for new users leading to insufficient personalisation, high timeliness requirements for live steaming recommendations, difficulty in maintaining user interest diversity, and complex e-commerce recommendation system chains. Traditional recommendation methods heavily rely on historical behaviour modeling, which struggles with the cold-start problem for new users. Live-streaming recommendations demand real-time responsiveness to rapidly changing content dynamics (e.g., host interactions, traffic fluctuations) within extremely short time windows (typically within 30 minutes) posing higher demands on the system's real-time perception and decision-making capabilities.

Additionally, the immersive single-feed format amplifies the challenge of maintaining content diversity, requiring a careful balance between multi-interest learning and the risk of content drift caused by exploratory recommendations. The current e-commerce recommendation system follows a multi-stage funnel architecture (recall–ranking–re-ranking), which often leads to inconsistent chains, high maintenance costs, and an overreliance on short-term value prediction. This leads users to fall into content homogenization fatigue.

To address these pain points, this project proposes leveraging large language models (LLMs) and large model technologies to achieve significant breakthroughs. On one hand, LLMs—with their vast knowledge base and few-shot reasoning capabilities—can infer new users' potential intentions from registration data and external knowledge, thereby alleviating cold-start issues. On the other hand, by integrating graph neural networks (GNNs) and full-lifecycle user behavior sequences for modeling social preferences, we aim to improve the accuracy of interest prediction.

Additionally, the project explores the generalization capabilities, long-context awareness, and end-to-end modeling strengths of large models to simplify the e-commerce recommendation chains, enhance adaptability to real-time changes, and improve exploratory recommendation effectiveness. The ultimate goal is to build a more streamlined system with more accurate recommendations, enhancing user experience and retention while driving sustainable business growth.