| Ddtcdr: Deep dual transfer cross domain recommendation P Li, A Tuzhilin Proceedings of the 13th International Conference on Web Search and Data …, 2020 | 252 | 2020 |
| Person-job fit: Adapting the right talent for the right job with joint representation learning C Zhu, H Zhu, H Xiong, C Ma, F Xie, P Ding, P Li ACM Transactions on Management Information Systems (TMIS) 9 (3), 1-17, 2018 | 157 | 2018 |
| Measuring the popularity of job skills in recruitment market: A multi-criteria approach T Xu, H Zhu, C Zhu, P Li, H Xiong Proceedings of the AAAI conference on artificial intelligence 32 (1), 2018 | 81 | 2018 |
| Dual metric learning for effective and efficient cross-domain recommendations P Li, A Tuzhilin IEEE Transactions on Knowledge and Data Engineering 35 (1), 321-334, 2021 | 49 | 2021 |
| PURS: Personalized unexpected recommender system for improving user satisfaction P Li, M Que, Z Jiang, Y Hu, A Tuzhilin Proceedings of the 14th ACM Conference on Recommender Systems, 279-288, 2020 | 47* | 2020 |
| Dual attentive sequential learning for cross-domain click-through rate prediction P Li, Z Jiang, M Que, Y Hu, A Tuzhilin Proceedings of the 27th ACM SIGKDD conference on knowledge discovery & data …, 2021 | 44 | 2021 |
| Towards Controllable and Personalized Review Generation P Li, A Tuzhilin Proceedings of the 2019 Conference on Empirical Methods in Natural Language …, 2019 | 41 | 2019 |
| Latent multi-criteria ratings for recommendations P Li, A Tuzhilin Proceedings of the 13th ACM Conference on Recommender Systems, 428-431, 2019 | 24 | 2019 |
| Latent unexpected recommendations P Li, A Tuzhilin ACM Transactions on Intelligent Systems and Technology (TIST) 11 (6), 1-25, 2020 | 12 | 2020 |
| Learning latent multi-criteria ratings from user reviews for recommendations P Li, A Tuzhilin IEEE Transactions on Knowledge and Data Engineering 34 (8), 3854-3866, 2020 | 11 | 2020 |
| Latent Modeling of Unexpectedness for Recommendations. P Li, A Tuzhilin RecSys (late-breaking results), 36-40, 2019 | 7 | 2019 |
| Adversarial learning for cross domain recommendations P Li, B Brost, A Tuzhilin ACM Transactions on Intelligent Systems and Technology 14 (1), 1-25, 2022 | 6 | 2022 |
| When Variety Seeking Meets Unexpectedness: Incorporating Variety-Seeking Behaviors into Design of Unexpected Recommender Systems P Li, A Tuzhilin Information Systems Research, 2023 | 5 | 2023 |
| Prompt tuning large language models on personalized aspect extraction for recommendations P Li, Y Wang, EH Chi, M Chen arXiv preprint arXiv:2306.01475, 2023 | 5 | 2023 |
| Leveraging Multi-Faceted User Preferences for Improving Click-Through Rate Predictions P Li Proceedings of the 15th ACM Conference on Recommender Systems, 864-868, 2021 | 4 | 2021 |
| Latent Unexpected and Useful Recommendation P Li, A Tuzhilin arXiv preprint arXiv:1905.01546, 2019 | 4 | 2019 |
| Deep multi-objective multi-stakeholder music recommendation M Unger, P Li, MC Cohen, B Brost, A Tuzhilin NYU Stern School of Business Forthcoming, 2021 | 3 | 2021 |
| Dual contrastive learning for efficient static feature representation in sequential recommendations P Li, M Que, A Tuzhilin IEEE Transactions on Knowledge and Data Engineering 36 (2), 544-555, 2023 | 1 | 2023 |
| Don’t need all eggs in one basket: Reconstructing composite embeddings of customers from individual-domain embeddings M Unger, P Li, S Sen, A Tuzhilin ACM Transactions on Management Information Systems 14 (2), 1-30, 2023 | 1 | 2023 |
| Hybrid Utility Function for Unexpected Recommendations P Li Proceedings of the 13th International Conference on Web Search and Data …, 2020 | 1 | 2020 |
Alexander TuzhilinProfessor of Information Systems, NYU在 stern.nyu.edu 的电子邮件经过验证
