字节跳动Recommendation Large Model Researcher | 推荐大模型算法工程师-电商-筋斗云人才计划
任职要求
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 h…工作职责
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 & 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. 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/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: 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. 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. The project will address these through deep research in model parameter scaling, content/user representation learning, multimodal content understanding, ultra-long sequence modeling, and generative recommendation models, driving systematic upgrades to recommendation models. 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 Involved Research Directions: Recommendation Algorithms, Large Recommendation Models. 团队介绍： 推荐与营销团队，主要负责国际电商商城推荐业务，涵盖商城首页、交易链路、商品详情页、店铺&橱窗等多个核心场景的信息流推荐业务，致力于每天为亿量级用户提供精准个性化商品、直播、短视频推荐服务；团队致力于解决现代推荐系统中各种有挑战的问题，通过算法不断提升用户体验和效率、创造更大的用户和社会价值。 课题背景/目标： 本项目旨在探索推荐领域下的大模型新范式，突破现在持续了较长时间的推荐模型结构和Infra的方案，且效果大幅好于现在的基线模型，在抖音短视频/直播/电商/头条等多个业务场景上得到应用。推荐领域的大模型是比较有挑战的事情，推荐对工程效率的要求更高，且用户的推荐体验上是个性化的，本课题会以下多个方向来做深入的研究，探索和建设推荐场景的大模型方案。 课题挑战/必要性： 自然语言领域LLM的出现，效果在众多垂直任务上都好于sota模型，从推荐领域看过去工业级推荐系统在较长的时间没有大幅的变化过。本项目旨在探索推荐领域下的大模型方案，改变现在持续了较长时间的推荐模型结构和Infra的基本范式，且效果大幅好于现在的模型，在抖音短视频/直播等多个业务场景上得到应用。但是怎么做好推荐领域的大模型也是一个比较有挑战的事情，推荐对工程效率的要求更高，且用户的推荐体验上是个性化的，以及如何短视频、直播等体裁上做号内容的表征也是需要被解决的问题，这里会从模型参数scaling up、内容和用户的表征学习、内容理解多模态、超长序列建模、生成式推荐模型等多个方向来做深入的研究，对推荐场景的模型做系统性的升级。 课题内容： 1、基于内容理解和用户行为的表征学习； 2、推荐模型参数和算力scaling up； 3、超长序列建模； 4、生成式推荐模型。 涉及研究方向：推荐算法、推荐大模型。
Partnership Development: Identify, negotiate, and secure partnerships with MNOs and MVNOs globally. Maintain strong relationships to ensure long-term collaboration and mutual growth. Contract Negotiation: Lead negotiations for partnership agreements, ensuring alignment with company strategy and objectives. Manage the end-to-end process from initial contact to contract signing. Market Expansion: Develop and implement strategies to expand the availability and reach of travel essentials products, driving market share growth in key regions. Product Enhancement: Collaborate with partners to develop and launch new products or services that meet the needs of travelers. Provide market insights to optimize product offerings and enhance customer satisfaction. Cross-functional Collaboration: Work closely with internal departments such as marketing, operations, and customer service to ensure seamless integration and execution of different projects. Performance Monitoring: Regularly review and analyze partner performance metrics. Provide insights and recommendations to improve outcomes and maximize ROI. Market Research: Conduct ongoing research on industry trends, competitor activities, and technological advancements in the telecom sector. Use this information to inform strategy and drive innovation. Reporting: Prepare regular reports on business partnership activities, market trends, and performance metrics for senior management.
1. Plan and execute various platform level promotions: include big promotion such as Black Friday or seasonal rebajas, to drive platform level uplift and make the sales reach to new height, and several monthly campaigns which serve different objectives: increase seasonal sub-categories, boost re-purchase rate, assist incubation, drive top brands or top assortment 2. Think from both seller and buyer points of view. 3. Leverage analytical data to point out the critical factors or measurable metrics to enhance promotion performance, improve SOP or raise new promotional initiatives. 4. Work with Product and User Experience Design team to identify improvement areas that can largely improve business results and better user experience.
1、负责搜索推荐流量策略的规划与设计，提升用户搜索和推荐的体验及转化效果。 2、分析用户行为数据，挖掘用户需求，优化流量分配策略，提高用户粘性和活跃度。 3、与技术、运营、数据团队紧密合作，推动策略的实施和迭代，确保产品目标的达成。 4、跟踪竞品动态，研究行业趋势，不断优化产品功能，提升搜索推荐的竞争力。 5、负责跨部门协调沟通，确保项目顺利进行，对产品效果进行持续监控和优化。 1、Responsible for planning and designing search and recommendation traffic strategies to enhance user experience and conversion. 2、Analyze user behavior data, uncover user needs, and optimize traffic allocation strategies to increase user stickiness and activity. 3、Collaborate with technical, operational, and data teams to drive strategy implementation and iteration, ensuring product goals are met. 4、Monitor competitors, research industry trends, and continuously optimize product features to enhance search and recommendation competitiveness. 5、Coordinate with cross-functional departments to ensure smooth project progress and continuously monitor and optimize product performance.
1、负责搜索推荐流量策略的规划与设计，提升用户搜索和推荐的体验及转化效果。 2、分析用户行为数据，挖掘用户需求，优化流量分配策略，提高用户粘性和活跃度。 3、与技术、运营、数据团队紧密合作，推动策略的实施和迭代，确保产品目标的达成。 4、跟踪竞品动态，研究行业趋势，不断优化产品功能，提升搜索推荐的竞争力。 5、负责跨部门协调沟通，确保项目顺利进行，对产品效果进行持续监控和优化。 1、Responsible for planning and designing search and recommendation traffic strategies to enhance user experience and conversion. 2、Analyze user behavior data, uncover user needs, and optimize traffic allocation strategies to increase user stickiness and activity. 3、Collaborate with technical, operational, and data teams to drive strategy implementation and iteration, ensuring product goals are met. 4、Monitor competitors, research industry trends, and continuously optimize product features to enhance search and recommendation competitiveness. 5、Coordinate with cross-functional departments to ensure smooth project progress and continuously monitor and optimize product performance.