Responsibilities

Team Introduction:
The team primarily focuses on recommendation services for the International E-commerce Mall, covering information flow recommendation in core scenarios such as the mall homepage, transaction funnels, product detail pages, stores, and showcases. Committed to providing hundreds of millions of users daily with precise and personalized recommendations for products, live streams, and short videos, the team dedicates itself to solving challenging problems in modern recommendation systems. Through algorithmic innovations, we continuously enhance user experience and efficiency, creating greater user and social value.

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Project Background/Objectives:
This project aims to explore new paradigms for large models in the recommendation field, breaking through the long-standing structures of recommendation models and Infra solutions, achieving significantly better performance than current baseline models, and applying them across multiple business scenarios such as Douyin short videos, LIVE, E-commerce, and Toutiao.
Developing large models for recommendation is particularly challenging due to the high demands on engineering efficiency and the personalized nature of user recommendation experiences. The project will conduct in-depth research across the following directions to explore and establish large model solutions for recommendation scenarios.

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Project Challenges/Necessity:
The emergence of LLMs in the natural language field has outperformed SOTA models in numerous vertical tasks. In contrast, industrial-grade recommendation systems have seen limited major innovations in recent years. This project seeks to revolutionize the long-standing paradigms of recommendation model architectures and Infra in the recommendation field, delivering models with significantly improved performance and applying them to scenarios like Douyin short video and LIVE.

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Key challenges include:

• High engineering efficiency requirements for recommendation systems
• Personalized nature of user recommendation experiences
• Effective content representation for media formats like short videos and live streams

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The project will address these through deep research in:

• Model parameter scaling
• Content/user representation learning
• Multimodal content understanding
• Ultra-long sequence modeling
• Generative recommendation models

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These efforts aim to drive systematic upgrades to recommendation models.

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Project Content:

1. Representation Learning Based on Content Understanding and User Behavior
2. Scaling of Recommendation Model Parameters and Computing
3. Ultra-Long Sequence Modeling
4. Generative Recommendation Models

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Involved Research Directions:
Recommendation Algorithms, Large Recommendation Models

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Qualifications

1. Got doctor degree, with priority given to candidates in computer science, mathematics, or related fields.
2. Possess a solid foundation in machine learning and coding skills, with in-depth research experience in machine learning, NLP, CV, etc., and be proficient in major algorithms and data structures.
3. Candidates who have participated in or led key projects in search, advertising, recommendation, or large model domains are preferred.
4. Preference for those who have published papers at top international conferences, including but not limited to KDD, SIGIR, RecSys, ACL, NeurIPS, etc.
5. Demonstrate strong problem analysis and solving abilities, passion for technology, and be eager to drive and tackle various challenges.

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