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 20 papers from applied research work done in Etsy and Yahoo Research.

Experimentation, Metrics and Causal Inference

  1. 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]
  2. 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]

Recommender Systems

  1. 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]
  2. 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]
  3. 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]
  4. X. Zhao, R. Louca, D. Hu and L. Hong. Learning Item-Interaction Embeddings for User Recommendations. DAPA at WSDM 2019. [Local Copy]
  5. 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]
  6. 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]
  7. 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]
  8. T. Chen, L. Hong, Y. Shi and Y. Sun. Joint Text Embedding for Personalized Content-based Recommendation. 2017. [ArXiv]
  9. 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]
  10. 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]
  11. L. Hong and A. Boz. An Unbiased Data Collection and Content Exploitation/Exploration Strategy for Personalization. 2016. [ArXiv]
  12. 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]
  13. 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. 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.
  2. 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]
  3. 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]
  4. L. Hong, Y. Shi and S. Rajan. Learning Optimal Card Ranking from Query Reformulation. 2016. [ArXiv]

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: