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2020 – today
- 2024
[c46]Yaochen Zhu
, Liang Wu, Qi Guo, Liangjie Hong, Jundong Li:
Collaborative Large Language Model for Recommender Systems. WWW 2024: 3162-3172- [i14]Xinyuan Wang, Liang Wu, Liangjie Hong, Hao Liu, Yanjie Fu:
LLM-Enhanced User-Item Interactions: Leveraging Edge Information for Optimized Recommendations. CoRR abs/2402.09617 (2024) - [i13]Jianqiang Shen, Yuchin Juan, Shaobo Zhang, Ping Liu, Wen Pu, Sriram Vasudevan, Qingquan Song, Fedor Borisyuk, Kay Qianqi Shen, Haichao Wei, Yunxiang Ren, Yeou S. Chiou, Sicong Kuang, Yuan Yin, Ben Zheng, Muchen Wu, Shaghayegh Gharghabi, Xiaoqing Wang, Huichao Xue, Qi Guo, Daniel Hewlett, Luke Simon, Liangjie Hong, Wenjing Zhang:
Learning to Retrieve for Job Matching. CoRR abs/2402.13435 (2024) - [i12]Yaochen Zhu, Liang Wu, Binchi Zhang, Song Wang, Qi Guo, Liangjie Hong, Luke Simon, Jundong Li:
Understanding and Modeling Job Marketplace with Pretrained Language Models. CoRR abs/2408.04381 (2024) - 2023
- [c45]Yaochen Zhu, Jing Ma, Liang Wu, Qi Guo, Liangjie Hong, Jundong Li:
Path-Specific Counterfactual Fairness for Recommender Systems. KDD 2023: 3638-3649 - [i11]Yaochen Zhu, Jing Ma, Liang Wu, Qi Guo, Liangjie Hong, Jundong Li:
Path-Specific Counterfactual Fairness for Recommender Systems. CoRR abs/2306.02615 (2023) - [i10]Yaochen Zhu, Liang Wu, Qi Guo, Liangjie Hong, Jundong Li:
Collaborative Large Language Model for Recommender Systems. CoRR abs/2311.01343 (2023) - 2022
- [j1]Zhiwei (Tony) Qin, Liangjie Hong, Rui Song, Hongtu Zhu, Mohammed Korayem, Haiyan Luo, Michael I. Jordan:
KDD 2022 Workshop on Decision Intelligence and Analytics for Online Marketplaces: Jobs, Ridesharing, Retail, and Beyond. SIGKDD Explor. 24(2): 78-80 (2022) - [c44]Zhiwei (Tony) Qin, Liangjie Hong, Rui Song, Hongtu Zhu, Mohammed Korayem, Haiyan Luo, Michael I. Jordan:
Decision Intelligence and Analytics for Online Marketplaces: Jobs, Ridesharing, Retail and Beyond. KDD 2022: 4898-4899 - [i9]Qinyi Zhu, Liang Wu, Qi Guo, Liangjie Hong:
Remote Work Optimization with Robust Multi-channel Graph Neural Networks. CoRR abs/2209.03150 (2022) - 2020
- [c43]Zenan Wang, Xuan Yin, Tianbo Li, Liangjie Hong:
Causal Meta-Mediation Analysis: Inferring Dose-Response Function From Summary Statistics of Many Randomized Experiments. KDD 2020: 2625-2635 - [c42]Ruocheng Guo, Xiaoting Zhao, Adam Henderson, Liangjie Hong, Huan Liu:
Debiasing Grid-based Product Search in E-commerce. KDD 2020: 2852-2860 - [c41]Liangjie Hong, Mounia Lalmas:
Tutorial on Online User Engagement: Metrics and Optimization. KDD 2020: 3551-3552 - [c40]Jianling Wang, Kaize Ding, Liangjie Hong, Huan Liu, James Caverlee:
Next-item Recommendation with Sequential Hypergraphs. SIGIR 2020: 1101-1110 - [c39]Jianling Wang, Raphael Louca, Diane Hu, Caitlin Cellier, James Caverlee, Liangjie Hong:
Time to Shop for Valentine's Day: Shopping Occasions and Sequential Recommendation in E-commerce. WSDM 2020: 645-653 - [c38]Xiaoting Zhao, Raphael Louca, Diane Hu, Liangjie Hong:
The Difference Between a Click and a Cart-Add: Learning Interaction-Specific Embeddings. WWW (Companion Volume) 2020: 454-460 - [c37]Md. Mehrab Tanjim, Congzhe Su, Ethan Benjamin, Diane Hu, Liangjie Hong, Julian J. McAuley:
Attentive Sequential Models of Latent Intent for Next Item Recommendation. WWW 2020: 2528-2534
2010 – 2019
- 2019
- [c36]Xuan Yin, Liangjie Hong:
The Identification and Estimation of Direct and Indirect Effects in A/B Tests through Causal Mediation Analysis. KDD 2019: 2989-2999 - [c35]Hao Jiang, Aakash Sabharwal, Adam Henderson, Diane Hu, Liangjie Hong:
Understanding the Role of Style in E-commerce Shopping. KDD 2019: 3112-3120 - [c34]Raphael Louca, Moumita Bhattacharya, Diane Hu, Liangjie Hong:
Joint Optimization of Profit and Relevance for Recommendation Systems in E-commerce. RMSE@RecSys 2019 - [c33]Nianqiao Ju, Diane Hu, Adam Henderson, Liangjie Hong:
A Sequential Test for Selecting the Better Variant: Online A/B testing, Adaptive Allocation, and Continuous Monitoring. WSDM 2019: 492-500 - [c32]Yixing Fan, Qingyao Ai, Zhaochun Ren, Liangjie Hong, Dawei Yin, Jiafeng Guo:
DAPA: The WSDM 2019 Workshop on Deep Matching in Practical Applications. WSDM 2019: 844-845 - [c31]Liangjie Hong, Mounia Lalmas:
Tutorial on Online User Engagement: Metrics and Optimization. WWW (Companion Volume) 2019: 1303-1305 - [i8]Andrew Stanton, Akhila Ananthram, Congzhe Su, Liangjie Hong:
Revenue, Relevance, Arbitrage and More: Joint Optimization Framework for Search Experiences in Two-Sided Marketplaces. CoRR abs/1905.06452 (2019) - 2018
- [c30]Diane Hu, Raphael Louca, Liangjie Hong, Julian J. McAuley:
Learning within-session budgets from browsing trajectories. RecSys 2018: 432-436 - [c29]Liang Wu, Diane Hu, Liangjie Hong, Huan Liu:
Turning Clicks into Purchases: Revenue Optimization for Product Search in E-Commerce. SIGIR 2018: 365-374 - [c28]Mounia Lalmas, Liangjie Hong:
Tutorial on Metrics of User Engagement: Applications to News, Search and E-Commerce. WSDM 2018: 781-782 - [i7]Xiaoting Zhao, Raphael Louca, Diane Hu, Liangjie Hong:
Learning Item-Interaction Embeddings for User Recommendations. CoRR abs/1812.04407 (2018) - 2017
- [c27]Qingyun Wu, Hongning Wang, Liangjie Hong, Yue Shi:
Returning is Believing: Optimizing Long-term User Engagement in Recommender Systems. CIKM 2017: 1927-1936 - [c26]Kamelia Aryafar, Devin Guillory, Liangjie Hong:
An Ensemble-based Approach to Click-Through Rate Prediction for Promoted Listings at Etsy. ADKDD@KDD 2017: 10:1-10:6 - [c25]Ting Chen, Yizhou Sun, Yue Shi, Liangjie Hong:
On Sampling Strategies for Neural Network-based Collaborative Filtering. KDD 2017: 767-776 - [c24]Yue Ning, Yue Shi, Liangjie Hong, Huzefa Rangwala, Naren Ramakrishnan:
A Gradient-based Adaptive Learning Framework for Efficient Personal Recommendation. RecSys 2017: 23-31 - [c23]Qian Zhao, Yue Shi, Liangjie Hong:
GB-CENT: Gradient Boosted Categorical Embedding and Numerical Trees. WWW 2017: 1311-1319 - [i6]Ting Chen, Liangjie Hong, Yue Shi, Yizhou Sun:
Joint Text Embedding for Personalized Content-based Recommendation. CoRR abs/1706.01084 (2017) - [i5]Ting Chen, Yizhou Sun, Yue Shi, Liangjie Hong:
On Sampling Strategies for Neural Network-based Collaborative Filtering. CoRR abs/1706.07881 (2017) - [i4]Kamelia Aryafar, Devin Guillory, Liangjie Hong:
An Ensemble-based Approach to Click-Through Rate Prediction for Promoted Listings at Etsy. CoRR abs/1711.01377 (2017) - 2016
- [i3]Liangjie Hong, Adnan Boz:
An Unbiased Data Collection and Content Exploitation/Exploration Strategy for Personalization. CoRR abs/1604.03506 (2016) - [i2]Liangjie Hong, Yue Shi, Suju Rajan:
Learning Optimal Card Ranking from Query Reformulation. CoRR abs/1606.06816 (2016) - 2015
- [c22]Mingjie Qian, Liangjie Hong, Yue Shi, Suju Rajan:
Structured Sparse Regression for Recommender Systems. CIKM 2015: 1895-1898 - 2014
- [c21]Xing Yi, Liangjie Hong, Erheng Zhong, Nathan Nan Liu, Suju Rajan:
Beyond clicks: dwell time for personalization. RecSys 2014: 113-120 - 2013
- [c20]Zaihan Yang, Liangjie Hong, Brian D. Davison:
Academic network analysis: a joint topic modeling approach. ASONAM 2013: 324-333 - [c19]Liangjie Hong, Shuang-Hong Yang:
The first workshop on user engagement optimization. CIKM 2013: 2559-2560 - [c18]Amr Ahmed, Liangjie Hong, Alexander J. Smola:
Nested Chinese Restaurant Franchise Process: Applications to User Tracking and Document Modeling. ICML (3) 2013: 1426-1434 - [c17]Liangjie Hong, Aziz S. Doumith, Brian D. Davison:
Co-factorization machines: modeling user interests and predicting individual decisions in Twitter. WSDM 2013: 557-566 - [c16]Amr Ahmed, Liangjie Hong, Alexander J. Smola:
Hierarchical geographical modeling of user locations from social media posts. WWW 2013: 25-36 - [e1]Liangjie Hong, Shuang-Hong Yang:
Proceedings of the 1st workshop on User engagement optimization, UEO@CIKM 2013, San Francisco, California, USA, November 1, 2013. ACM 2013, ISBN 978-1-4503-2421-2 [contents] - 2012
- [c15]Liangjie Hong, Ron Bekkerman, Joseph Adler, Brian D. Davison:
Learning to rank social update streams. SIGIR 2012: 651-660 - [c14]Liangjie Hong, Amr Ahmed, Siva Gurumurthy, Alexander J. Smola, Kostas Tsioutsiouliklis:
Discovering geographical topics in the twitter stream. WWW 2012: 769-778 - [i1]Liangjie Hong:
A Tutorial on Probabilistic Latent Semantic Analysis. CoRR abs/1212.3900 (2012) - 2011
- [c13]Dawei Yin, Liangjie Hong, Zhenzhen Xue, Brian D. Davison:
Temporal Dynamics of User Interests in Tagging Systems. AAAI 2011: 1279-1285 - [c12]Dawei Yin, Liangjie Hong, Brian D. Davison:
Structural link analysis and prediction in microblogs. CIKM 2011: 1163-1168 - [c11]Liangjie Hong, Dawei Yin, Jian Guo, Brian D. Davison:
Tracking trends: incorporating term volume into temporal topic models. KDD 2011: 484-492 - [c10]Liangjie Hong, Byron Dom, Siva Gurumurthy, Kostas Tsioutsiouliklis:
A time-dependent topic model for multiple text streams. KDD 2011: 832-840 - [c9]Dawei Yin, Liangjie Hong, Xiong Xiong, Brian D. Davison:
Link formation analysis in microblogs. SIGIR 2011: 1235-1236 - [c8]Liangjie Hong, Ovidiu Dan, Brian D. Davison:
Predicting popular messages in Twitter. WWW (Companion Volume) 2011: 57-58 - [c7]Dawei Yin, Liangjie Hong, Brian D. Davison:
Exploiting session-like behaviors in tag prediction. WWW (Companion Volume) 2011: 167-168 - 2010
- [c6]Liangjie Hong, Brian D. Davison:
Empirical study of topic modeling in Twitter. SOMA@KDD 2010: 80-88 - [c5]Dawei Yin, Zhenzhen Xue, Liangjie Hong, Brian D. Davison:
A probabilistic model for personalized tag prediction. KDD 2010: 959-968 - [c4]Zaihan Yang, Liangjie Hong, Brian D. Davison:
Topic-driven multi-type citation network analysis. RIAO 2010: 24-31
2000 – 2009
- 2009
- [c3]Liangjie Hong, Zaihan Yang, Brian D. Davison:
Incorporating Participant Reputation in Community-Driven Question Answering Systems. CSE (4) 2009: 475-480 - [c2]Jian Wang, Liangjie Hong, Brian D. Davison:
RSDC'09: Tag Recommendation Using Keywords and Association Rules. DC@PKDD/ECML 2009 - [c1]Liangjie Hong, Brian D. Davison:
A classification-based approach to question answering in discussion boards. SIGIR 2009: 171-178
Coauthor Index
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last updated on 2024-10-01 21:42 CEST by the dblp team
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