Liang Tang's Homepage

Profile

I'm a machine learning scientist and engineer. My research domains include Retrieval and Ranking Relevance, Personalized Recommendation Systems. Methods include ranking models, retrieval / candidate selection models, reinforcement learning, predictive / propensity models, deep learning models, among others. Most recently, I am an engineer manager at Google Recommendation AI team, which provides personalized recommendation solutions for Google Cloud customers. Before that, I was an engineering manager at Google Display Ads since 2022, focusing on building the next generation of Google Ads products. I have led multiple projects and cross-team / cross-org collaborations, working with PMs / TPMs / Directors / Sales, leading to successful Google Ads product launches.

Prior to joining Google, I was an engineer in the Feed Relevance team at LinkedIn, focusing on feature engineering of the LinkedIn feed ranking model to improve the user engagement on LinkedIn home feeds. Some parts of my old work are summarized in the blog "Making Your Feed More Relevant": part1 part2.

I published 20+ articles in machine learning in venues including ACM KDD, SIGIR, CIKM, RecSys and IEEE TKDE. I also served as a PC member of several top research conferences in data mining, including SIGKDD, CIKM, WSDM, ECMLPKDD and a reviewer for prestigious journals, include: ACM TKDD, IEEE TKDE and KAIS.

My Curriculum Vitae is available upon request.

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Research Interest

Broadly interested in data and information analysis including data mining, machine learning and information retrieval techniques. More specifically, I focus on user modeling and personalization in recommender systems, online advertising, multi-arm bandit algorithms, event mining that discovers and summarizes the hidden relations of the event data, streaming data, time series data and textual data. The summary of my work in event mining can be found in ACM KDD 2017 tutorial System Event Mining: Algorithms and Applications: video1,video2.

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Professional Service

PC member of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2024).
PC member of the 17th ACM International Conference on Web Search and Data Mining (WSDM 2024).
PC member of the Conference on ECML PKDD 2023 (ECMLPKDD 2023).
PC member and session chair of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2023).
PC member of the 31th ACM International Conference on Information and Knowledge Management (CIKM 2022).
PC member of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2022).
PC member of the 15th ACM International Conference on Web Search and Data Mining (WSDM 2022).
PC member of the 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2021).
PC member of the 26th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2020).
PC member of the 25th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2019).
PC member of the 1st International Workshop on AI and Data Mining for Services (ADMS) in association with ICSOC 2018.
PC member of the 24th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2018).
PC member of the 23rd ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2017).
Invited Reviewer of ACM Transactions on Social Computing.
Invited Reviewer of ACM Transactions on Knowledge Discovery From Data (TKDD).
Invited Reviewer of IEEE Transactions on Knowledge and Data Engineering (TKDE).
Invited Reviewer of IEEE Transactions on Emerging Topics in Computing (TETC).
Invited Reviewer of Data and Knowledge Engineering (DKE).
Invited Reviewer of Knowledge and Information Systems(KAIS).
PC member of the 24th ACM International Conference on Information and Knowledge Management (CIKM 2015).
PC member of the 2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2015).
PC member of the IEEE International Conference on Services Computing 2014, 2015 and 2016.
Session Chair of ``Data Streams and Ranking" in ACM CIKM 2013.
PC member of the 10th IEEE International Conference on Machine Learning and Applications (ICMLA 2011).

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Recent Publication

Liang Tang, Bo Long, Bee-Chung Chen, and Deepak Agarwal. "An Empirical Study on Recommendation with Multiple Types of Feedback", in Proceedings of the 22nd ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2016), San Francisco, California, Aug. 13-17, 2016 (oral presentation in applied science track, acceptance rate: 40/331=12.0%).
Liang Tang, Yexi Jiang, Lei Li, Chunqiu Zeng and Tao Li. "Personalized Recommendation via Parameter-Free Contextual Bandits", in Proceedings of the 38th Annual International ACM SIGIR Conference (SIGIR 2015), Santiago de Chile., Aug. 9-13, 2015 (oral presentation, acceptance rate: 70/351=20.0%) [slides].
Lei Li, Chao Shen, Long Wang, Li Zheng, Yexi Jiang, Liang Tang, Hongtai Li, Longhui Zhang, Chunqiu Zeng and Tao Li."iMiner: Mining Inventory Data for Intelligent Management", in Proceedings of the 23th ACM Conference on Information and Knowledge Management (CIKM 2014), Shanghai, China, Nov. 2014, pages 2057-2059.
Liang Tang, Yexi Jiang, Lei Li and Tao Li. "Ensemble Contextual Bandits for Personalized Recommendation", in Proceedings of the 8th ACM Conference on Recommender Systems(RecSys 2014) [slides], Silicon Valley, CA, USA, Oct. 2014, pages 73-80.
Li Zheng, Chunqiu Zeng, Lei Li, Yexi Jiang, Wei Xue, Jingxuan Li, Chao Shen, Wubai Zhou, Hongtai Li, Liang Tang, Tao Li, Bing Duan, Ming Lei, and Pengnian Wang. "Applying Data Mining Techniques to Address Critical Process Optimization Needs in Advanced Manufacturing", in Proceedings of the 20th ACM Conference on Knowledge Discovery and Data Mining (KDD 2014), New York, USA, Aug. 2014, pages 1739-1748.

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News

2021.4 Invited as a program committee member of the ACM SIGKDD Conference on Knowledge Discovery and Data Mining since 2017 (KDD).

2018.7 Invited as a program committee member of the 1st International Workshop on AI and Data Mining for Services (ADMS) in association with ICSOC 2018.

2016.5 Our paper "An Empirical Study on Recommendation with Multiple Types of Feedback" has been accepted in ACM KDD 2016.

2015.8 Our book "Event Mining: Algorithms and Applications" has been officially announced.

2015.7 Our book "Maximizing Management Performance and Quality with Service Analytics" has been officially announced.

2015.4 Our paper entitled "Personalized Recommendation via Parameter-Free Contextual Bandits" has been accepted for oral presentation and inclusion in the proceedings of the 2015 ACM SIGIR Conference.

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Last Updated: 2024-07-19