Research

[DBLP] [Google Scholar] [Publication List Sorted by Year]

Current Research Interests

My research interests include search and recommendation systems on a large scale as well as how to conduct effective and efficient online experiments to optimize user engagement in modern Internet platforms. I have published more than 50 papers from applied research work done in LinkedIn, Etsy and Yahoo Research.

Recommender Systems

  1. X. Wang, L. Wu, L. Hong, H. Liu and Y. Fu. LLM-Enhanced User-Item Interactions: Leveraging Edge Information for Optimized Recommendations. ACM Transactions on Intelligent Systems and Technology, Volume 16, Feb, 2025. [DOI]
  2. Z. Zheng, S. Wang, Z. Chen, Y. Zhu, Y. He, L. Hong, Q. Guo and J. Li. CoRAG: Enhancing Hybrid Retrieval-Augmented Generation Through a Cooperative Retriever Architecture. In Proceedings of The 2025 Conference on Empirical Methods in Natural Language Processing (EMNLP 2025), Suzhou, China, November, 2025. [DOI]
  3. Y. Juan, J. Shen, S. Zhang, Q. Shen, C. Johnson, L. Simon, L. Hong and W. Zhang. Scaling Retrieval for Web-Scale Recommenders: Lessons from Inverted Indexes to Embedding Search. In Proceedings of the Nineteenth ACM Conference on Recommender Systems (RecSys 2025), Prague, Czech Republic, September, 2025. [DOI]
  4. P. Liu, R. Arora, X. Shi, B. Le, Q. Shen, J. Shen, C. Jiang, N. Zhiltsov, P, Bannur, Y. Zhu, L. Dong, H. Wei, Q. Guo, L. Simon, L. Hong and W. Zhang. A Scalable and Efficient Signal Integration System for Job Matching. In Proceedings of the 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining V. 2 (KDD 2025), Toronto, Canada, August, 2025. [DOI]
  5. Y. Zhu, L. Wu, B. Zhang, S. Wang, Q. Guo, L. Hong, L. Simon and and J. Li. Understanding and Modeling Job Marketplace with Pretrained Language Models. In Proceedings of the 33rd ACM International Conference on Information and Knowledge Management (CIKM 2024), Boise, ID, USA, October 2024. [DOI] [Earlier ArVix Version]
  6. Y. Zhu, L. Wu, Q. Guo, L. Hong and J. Li. Collaborative Large Language Model for Recommender Systems. In Proceedings of the ACM on Web Conference 2024 (WWW 2024), Singapore, May 2024. [DOI] [Earlier ArVix Version]
  7. X. Wang, L. Wu, L. Hong, H. Liu and Y. Fu. LLM-Enhanced User-Item Interactions: Leveraging Edge Information for Optimized Recommendations. ArXiv. 2024.
  8. Y. Zhu, J. Ma, L. Wu, Q. Guo, L. Hong and J. Li. Path-Specific Counterfactual Fairness for Recommender Systems. In Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2023), Long Beach CA, August 2023. [DOI] [Earlier ArVix Version]
  9. Q. Zhu, L. Wu, Q. Guo and L. Hong. Remote Work Optimization with Robust Multi-channel Graph Neural Networks. ArXiv. 2022.
  10. J. Wang, R. Louca, D. Hu, C. Cellier, J. Caverlee and L. Hong. Time to Shop for Valentine’s Day: Shopping Occasions and Sequential Recommendation in E-commerce. In the proceedings of the 13th ACM International Conference on Web Search and Data Mining (WSDM 2020), Houston, Texas, Feb, 2020. (Full Paper, 15% Acceptance) [Local Copy] [DOI]
  11. R. Louca, M. Bhattacharya, D. Hu and L. Hong. Joint Optimization of Profit and Relevance for Recommendation Systems in E-commerce. In the proceedings of RMSE Workshop 2019 at RecSys 2019. [Local Copy]
  12. H. Jiang, A. Sabharwal, A. Henderson, D. Hu and L. Hong. Understanding the Role of Style in E-commerce Shopping. In the proceedings of the 25th SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2019), Anchorage, Alaska, August, 2019. (Full Paper, 20% Acceptance) [Local Copy] [DOI]
  13. X. Zhao, R. Louca, D. Hu and L. Hong. Learning Item-Interaction Embeddings for User Recommendations. DAPA at WSDM 2019. [Local Copy]
  14. D. Hu, R. Louca, L. Hong and J. McAuley. Learning Within-Session Budgets from Browsing Trajectories. In the proceedings of the 12th ACM Conference on Recommender Systems (RecSys 2018), Vancouver, Canada, October, 2018. (Short Paper, 25% Acceptance) [Local Copy] [DOI]
  15. Q. Wu, H. Wang, L. Hong and Y. Shi. Returning is Believing: Optimizing Long-term User Engagement in Recommender Systems. In the proceedings of the 26th ACM International Conference on Information and Knowledge Management (CIKM 2017), Singapore, November, 2017. (Full Paper, 21% Acceptance) [Local Copy] [DOI]
  16. Y. Ning, Y. Shi, L. Hong, H. Rangwala and N. Ramakrishnan. A Gradient-based Adaptive Learning Framework for Efficient Personal Recommendation. To appear in the proceedings of the 11th ACM Conference on Recommender Systems (RecSys 2017), Como, Italy, August, 2017. (Full Paper, 20.8% Acceptance) [Local Copy] [DOI]
  17. T. Chen, L. Hong, Y. Shi and Y. Sun. Joint Text Embedding for Personalized Content-based Recommendation. 2017. [ArXiv]
  18. T. Chen, Y. Sun, Y. Shi and L. Hong. On Sampling Strategies for Neural Network-based Collaborative Filtering. In the proceedings of the 23rd SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2017), Halifax, Nova Scotia, August, 2017. (Full Paper, 17% Acceptance) [Local Copy] [Local Supplementary] [DOI] [Code]
  19. Q. Zhao, Y. Shi and L. Hong. GB-CENT: Gradient Boosted Categorical Embedding and Numerical Trees. In the proceedings of the 26th International Conference on World Wide Web (WWW 2017), Perth, Australia, April, 2017. (Full Paper, 17% Acceptance) [Local Copy] [DOI]
  20. L. Hong and A. Boz. An Unbiased Data Collection and Content Exploitation/Exploration Strategy for Personalization. 2016. [ArXiv]
  21. M. Qian, L. Hong, Y. Shi and S. Rajan. Structured Sparse Regression for Recommender Systems [Short Paper]. In the proceedings of the 24th ACM International  Conference on Information & Knowledge Management (CIKM 2015). Melbourne, Australia.[Local Copy] [DOI]
  22. X. Yi, L. Hong, E. Zhong, NN. Liu and S. Rajan. Beyond Clicks: Dwell Time in Personalization. In the proceedings of the 8th ACM Conference on Recommender Systems (RecSys 2014), Foster City, Silicon Valley, USA, October, 2014. (Full Paper, 23% Acceptance) [Local Copy] [DOI]

Search and Ranking

  1. P. Liu, J. Shen, Q. Shen, C. Yao, K. Kao, D. Xu, R. Arora, B. Zheng, C. Johnson, L. Hong, J. Wu and W. Zhang. Powering Job Search at Scale: LLM-Enhanced Query Understanding in Job Matching Systems. In Proceedings of the 34th ACM International Conference on Information and Knowledge Management (CIKM 2025), Seoul, Korea, November, 2025. [DOI]
  2. J. Shen, Y. Juan, P. Liu, W. Pu, S. Zhang, Q. Shen, L. Hong and W. Zhang. Learning Links for Adaptable and Explainable Retrieval. In Proceedings of the 33rd ACM International Conference on Information and Knowledge Management (CIKM 2024), Boise, ID, USA, October 2024. [DOI]
  3. J. Shen, Y. Juan, S. Zhang, P. Liu, W. Pu, S. Vasudevan, Q. Song, F. Borisyuk, K. Qianqi Shen, H, Wei, Y. Ren, Y. S. Chiou, S. Kuang, Y. Yin, B. Zheng, M. Wu, S. Gharghabi, X. Wang, H. Xue, Q. Guo, D. Hewlett, L. Simon, L. Hong and W. Zhang. Learning to Retrieve for Job Matching. ArXiv. 2024.
  4. A. Stanton, A. Ananthram, C. Su and L. Hong. Revenue, Relevance, Arbitrage and More: Joint Optimization Framework for Search Experiences in Two-Sided Marketplaces. ArXiv. 2019.
  5. L. Wu, D. Hu, L. Hong and H. Liu. Turning Clicks into Purchases: Revenue Optimization for Product Search in E-Commerce. In the proceedings of the 41st International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 2018), Ann Arbor, Michigan, U.S.A. July 8-12, 2018. (Full Paper, 21% Acceptance) [Local Copy] [DOI]
  6. K. Aryafar, D. Guillory and L. Hong. An Ensemble-based Approach to Click-Through Rate Prediction for Promoted Listings at Etsy. In the proceedings of AdKDD & TargetAd 2017 workshop, held in conjunction with the 23rd SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2017), Halifax, Nova Scotia, August, 2017. [PDF]
  7. L. Hong, Y. Shi and S. Rajan. Learning Optimal Card Ranking from Query Reformulation. 2016. [ArXiv]

Experimentation, Metrics and Causal Inference

  1. Y. Zhu, J. Ma, L. Wu, Q. Guo, L. Hong and J. Li. Causal Effect Estimation with Mixed Latent Confounders and Post-treatment Variables. In Proceedings of the Thirteenth International Conference on Learning Representations (ICLR 2025), Singapore, April, 2025. [DOI]
  2. Z. Wang, X. Yin, T. Li, and L. Hong. Causal Meta-Mediation Analysis: Inferring Dose-Response Function From Summary Statistics of Many Randomized Experiments. In Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (KDD 2020). Virtual Event, August 2020. (Full Paper, 16% Acceptance) [DOI] [Local Copy]
  3. X. Yin and L. Hong. The Identification and Estimation of Direct and Indirect Effects in A/B Tests through Causal Mediation Analysis. In the proceedings of the 25th SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2019), Anchorage, Alaska, August, 2019. (Full Paper, Oral Presentation, 6.4% Acceptance) [Local Copy] [DOI]
  4. N. Ju, D. Hu, A. Henderson and L. Hong. A Sequential Test for Selecting the Better Variant – Online A/B testing, Adaptive Allocation, and Continuous Monitoring. In the proceedings of the 12th ACM International Conference on Web Search and Data Mining (WSDM 2019), Melbourne, Australia, Feb, 2019. (Full Paper, 16% Acceptance) [Local Copy] [DOI]

Machine Learning Systems

  1. A. Stanton, L. Hong and M. Rajashekhar. Buzzsaw: A System for High Speed Feature Engineering. In the proceedings of the 1st SysML Conference, Stanford, CA, Feb, 2018. [Local Copy]

Research During Ph.D. Period

My dissertation research lies primarily on the interface between social media and applied machine learning where I develop state-of-the-art machine learning techniques to analyze social media data on a large scale in an effective and efficient way. My dissertation topic is “Mining and Understanding Online Conversational Media” (Whole Dissertation), focusing on the following research topics:

Other topics that I have contributed to: