腾讯高级研究员，专注于推荐系统与计算广告领域。在内容推荐（信息流、视频、动漫）和广告推荐（电商、游戏行业）上均有丰富的实战经验，覆盖召回、粗排、精排、特征建模全链路。研究兴趣：机器学习、深度学习、推荐算法、NLP、大语言模型（LLM）。2022年入选深圳创新中心专家人才库。
I am a Senior Researcher at Tencent, working on recommendation systems and computational advertising. I have extensive hands-on experience in both content recommendation (feeds, video, comics) and ad recommendation (e-commerce, gaming), covering the full pipeline of retrieval, pre-ranking, ranking, and feature engineering. Research interests: Machine Learning, Deep Learning, Recommendation Algorithms, NLP, LLM. I was selected for the Shenzhen Innovation Center Expert Talent Pool in 2022.
硕士毕业于国防科学技术大学（推荐算法方向），本科毕业于北京邮电大学。曾在华为担任高级算法工程师，负责大数据营销平台的开发。
I received my M.S. from National University of Defense Technology (NUDT) in Recommendation Algorithms, and B.S. from Beijing University of Posts and Telecommunications (BUPT). Before Tencent, I worked at Huawei as a Senior Algorithm Engineer on big data marketing platforms.
教育背景Education
国防科学技术大学 — 硕士National University of Defense Technology (NUDT) — M.S.2013 – 2016
信息系统与管理学院，研究方向：推荐算法 / 大数据 / 机器学习School of Information Systems and Management. Research: Recommendation Algorithms / Big Data / ML
北京邮电大学 — 本科Beijing University of Posts and Telecommunications (BUPT) — B.S.2009 – 2013
管理学院，电子商务专业School of Management, E-Commerce
News
Mar 2026新专利"用户行为序列分析和广告质量评估的多Agent构建"已提交New patent filed: "Multi-Agent Construction for User Behavior Sequence Analysis and Ad Quality Evaluation"
Jan 2026新专利"生成式推荐中的模型框架设计"已提交New patent filed: "Model Framework Design for Generative Recommendation"
2025两项新专利已提交（LLM重排、多模态+ID兴趣建模）Two new patents filed (LLM re-ranking, multimodal + ID interest modeling)
探索将大语言模型融入推荐系统全链路：利用LLM进行用户兴趣理解、候选集重排序和推荐理由生成，结合传统召回/精排模型构建混合推荐架构。Exploring LLM integration across the full recommendation pipeline: leveraging LLMs for user interest understanding, candidate re-ranking, and recommendation explanation generation.
基于Capsule Network和Transformer的用户多兴趣抽取方法，结合行为序列时序建模，捕获用户动态兴趣演化。Multi-interest extraction via Capsule Networks and Transformers, combined with temporal sequential modeling to capture dynamic user interest evolution.
Transformer多兴趣建模Multi-Interest序列推荐Sequential Rec
广告电商行业模型E-commerce Ad Industry Model已上线Shipped
从0搭建行业模型，使用MoE/MMoE/CGC多任务融合建模，引入STAR/DIN-attention/FwFFM等模型优化。上线效果：电商行业消耗+3.48%，GMV+5.93%，带动大盘GMV+1.48%。Built industry-specific models from scratch using MoE/MMoE/CGC multi-task learning, with STAR/DIN-attention/FwFFM optimizations. Results: +3.48% ad spend, +5.93% GMV, +1.48% overall platform GMV.
TencentMoE/MMoE/CGCSTARDIN-attentionFwFFM
信息流推荐系统Feed Recommendation System已上线Shipped
负责波洞App（从0到1）、手Q动漫、企鹅电竞视频Feeds的推荐算法，从LR到NN模型演进，经历冷启动、策略迭代等各阶段。对比产品部算法中台：波洞视频观看时长+68%，点击率+41%。Led recommendation systems for Boodo App (from scratch), QQ Comics, Penguin Esports video feeds. Evolved from LR to deep NN models. vs. product algorithm team: +68% watch time, +41% CTR.
TencentDeep NN冷启动Cold StartFeeds
UDM大数据营销平台UDM Big Data Marketing Platform已上线Shipped
负责Lookalike用户挖掘模块和实时营销时机事件引擎开发，基于Hadoop/Spark/Kafka构建实时敏捷数字化营销解决方案。Developed Lookalike user mining module and real-time marketing event engine using Hadoop/Spark/Kafka for agile digital marketing solutions.
HuaweiHadoopSparkKafkaLookalike
💡 深耕推荐系统8年+，持续关注LLM与推荐的深度融合，以及多目标建模、因果推断在广告推荐中的前沿应用。💡 8+ years in RecSys, continuously exploring deep integration of LLMs with recommendation, and frontier applications of multi-objective modeling and causal inference in ad recommendation.
多模态广告内容理解与匹配Multimodal Ad Content Understanding & Matching进行中In Progress
融合广告素材的图像、视频、文本等多模态信息，通过CLIP/BLIP类模型提取语义特征，构建跨模态用户-广告匹配模型。Fusing image, video, and text from ad creatives, extracting semantic features via CLIP/BLIP-family models, building cross-modal user-ad matching models.
CLIPBLIP跨模态匹配Cross-Modal
多模态+ID融合的用户生成式兴趣建模Multimodal + ID Fusion for Generative User Interest Modeling进行中In Progress
将多模态内容表征与传统ID Embedding进行深度融合，通过生成式建模捕获用户多维度兴趣，解决冷启动和跨域推荐问题。已申请相关专利。Deep fusion of multimodal content representations with traditional ID embeddings, capturing multi-dimensional user interests via generative modeling. Related patent filed.
构建DSSM及自监督Embedding特征，泛娱乐特征及电商embedding特征应用于召回/粗排/精排全链路。上线效果：电商行业消耗+0.88%，GMV+1.38%。Built DSSM and self-supervised embedding features applied across retrieval/pre-ranking/ranking pipeline. Results: +0.88% ad spend, +1.38% GMV.
覆盖A股/港股/美股的技术分析平台，支持K线形态识别、MA20偏离度/MACD/RSI等多指标分析、日线与周线双周期信号生成，以及基本面数据和新闻聚合展示。Technical analysis platform covering A-shares, HK, and US stocks. Features candlestick pattern recognition, MA20/MACD/RSI multi-indicator analysis, dual-timeframe signal generation, and fundamental data with news aggregation.
基于强化学习的A股量化交易策略RL-Based Quantitative Trading Strategy for A-Shares进行中In Progress
利用PPO/DQN强化学习算法，结合技术指标和市场情绪因子，构建自动化交易策略。Building automated trading strategies using PPO/DQN reinforcement learning with technical indicators and market sentiment factors.
搭建基于ROS2的家用机器人原型，集成SLAM导航、语音交互和视觉识别能力。Building a home robot prototype based on ROS2, integrating SLAM navigation, voice interaction, and visual recognition.
探索用大语言模型作为机器人高层任务规划器，通过自然语言指令分解复杂任务。Exploring LLMs as high-level task planners for robots, decomposing complex tasks from natural language instructions.
2022入选深圳创新中心专家人才库Selected for Shenzhen Innovation Center Expert Talent Pool
2018–2024腾讯连续多年四/五星员工Tencent 4/5-Star Employee for multiple consecutive years
2020《特征挖掘工程》获 TEG 2020H2 SEVP业务突破奖Feature Mining Engineering — TEG 2020H2 SEVP Business Breakthrough Award
2019《开放推荐引擎》获 TEG 2019H2 SEVP业务突破奖Open Recommendation Engine — TEG 2019H2 SEVP Business Breakthrough Award
2016华为明日之星、两次部门软件之星Huawei Star of Tomorrow, twice Department Software Star
2015Twitter数据分析平台获军事创新二等奖Twitter Analytics Platform — Military Innovation 2nd Prize
2015
Kaggle — San Francisco Crime Classification, Top 10% (2335 teams)
2012全国大学生"创意、创新、创业"电子商务比赛北京赛区三等奖National E-Commerce Innovation Competition, Beijing Region 3rd Prize
简历Curriculum Vitae
基本信息General Information
姓名Name郭亮 (Liang Guo)Liang Guo (郭亮)
语言Languages中文（母语）、英语（流利）Mandarin (native), English (fluent)
研究方向Research推荐系统、计算广告、深度学习、NLP、LLMRecommendation Systems, Computational Advertising, Deep Learning, NLP, LLM
工作经历Experience
2018 – Present
高级研究员Senior Researcher
腾讯，深圳Tencent, Shenzhen
广告电商行业模型（2020–2022，负责人）：从0搭建行业模型，使用MoE/MMoE/CGC多任务融合建模，引入STAR/DIN-attention/FwFFM等模型优化。上线效果：电商行业消耗+3.48%，GMV+5.93%，带动大盘GMV+1.48%。E-commerce Ad Industry Model (2020–2022, Lead): Built industry-specific models from scratch using MoE/MMoE/CGC multi-task learning, with STAR/DIN-attention/FwFFM optimizations. Results: +3.48% ad spend, +5.93% GMV, +1.48% overall platform GMV.
电商特征图谱构建（2019–2020，负责人）：构建DSSM及自监督Embedding特征，泛娱乐特征及电商embedding特征应用于召回/粗排/精排全链路。上线效果：电商行业消耗+0.88%，GMV+1.38%。E-commerce Feature Graph (2019–2020, Lead): Built DSSM and self-supervised embedding features applied across retrieval/pre-ranking/ranking pipeline. Results: +0.88% ad spend, +1.38% GMV.
信息流推荐（2018–2020，负责人）：负责波洞App（从0到1）、手Q动漫、企鹅电竞视频Feeds的推荐算法，从LR到NN模型演进，经历冷启动、策略迭代等各阶段。对比产品部算法中台：波洞视频观看时长+68%，点击率+41%。Feed Recommendation (2018–2020, Lead): Led recommendation systems for Boodo App (from scratch), QQ Comics, Penguin Esports video feeds. Evolved from LR to deep NN models. vs. product algorithm team: +68% watch time, +41% CTR.
内部行家咨询：在腾讯"行家"平台提供专题咨询，累计46次咨询，评分98.55/100，2023年度优秀行家。Internal Expert Consulting: Provided consulting on Tencent's Expert platform. 46 consultations, rated 98.55/100, named 2023 Annual Outstanding Expert.
2016 – 2018
高级算法工程师Senior Algorithm Engineer
华为，深圳Huawei, Shenzhen
UDM大数据营销平台：负责Lookalike用户挖掘模块和实时营销时机事件引擎开发，基于Hadoop/Spark/Kafka构建实时敏捷数字化营销解决方案。UDM Big Data Marketing Platform: Developed Lookalike user mining module and real-time marketing event engine using Hadoop/Spark/Kafka for agile digital marketing solutions.
教育背景Education
2013 – 2016
硕士M.S.
国防科学技术大学，信息系统与管理学院National University of Defense Technology (NUDT)
研究方向：推荐算法 / 大数据 / 机器学习Research: Recommendation Algorithms / Big Data / Machine Learning
2009 – 2013
本科B.S.
北京邮电大学，管理学院，电子商务专业Beijing University of Posts and Telecommunications (BUPT), E-Commerce
