Table of Links Abstract and 1 Introduction Methodology
Experiments
Conclusion and References 4. CONCLUSION In this paper, we propose the framework of Deep Mutual Learning across task towers(DML), which is compatible with various backbone multi-task networks. Extensive offline experiments help to verify DML’s effectiveness on multiple real-world datasets and across various base models. Moreover, thorough ablation studies are carried out to verify and understand the value of each newly introduced module. Finally, DML achieves significant online gains and has already been deployed to the online platform. REFERENCES [1] Martín Abadi, Paul Barham, Jianmin Chen, Zhifeng Chen, Andy Davis, Jeffrey Dean, Matthieu Devin, Sanjay Ghemawat, Geoffrey Irving, Michael Isard, et al. 2016. Tensorflow: a system for large-scale machine learning.. In Osdi, Vol. 16. Savannah, GA, USA, 265–283. [2] Raman Arora, Amitabh Basu, Poorya Mianjy, and Anirbit Mukherjee. 2018. Understanding Deep Neural Networks with Rectified Linear Units. In 6th International Conference on Learning Representations, ICLR 2018, Vancouver, BC, Canada, April 30 - May 3, 2018, Conference Track Proceedings. OpenReview.net. https://openreview.net/forum?id=B1J_rgWRW [3] Rich Caruana. 1997. Multitask learning. Machine learning 28, 1 (1997), 41–75. [4] Paul Covington, Jay Adams, and Emre Sargin. 2016. Deep neural networks for youtube recommendations. In Proceedings of the 10th ACM conference on recommender systems. 191–198. [5] Ke Ding, Xin Dong, Yong He, Lei Cheng, Chilin Fu, Zhaoxin Huan, Hai Li, Tan Yan, Liang Zhang, Xiaolu Zhang, et al. 2021. MSSM: a multiple-level sparse sharing model for efficient multi-task learning. In Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval. 2237–2241. [6] Long Duong, Trevor Cohn, Steven Bird, and Paul Cook. 2015. Low resource dependency parsing: Cross-lingual parameter sharing in a neural network parser. In Proceedings of the 53rd annual meeting of the Association for Computational Linguistics and the 7th international joint conference on natural language processing (volume 2: short papers). 845–850. [7] Peter A Flach, José Hernández-Orallo, and Cèsar Ferri Ramirez. 2011. A coherent interpretation of AUC as a measure of aggregated classification performance. In ICML. [8] F Maxwell Harper and Joseph A Konstan. 2015. The movielens datasets: History and context. Acm transactions on interactive intelligent systems (tiis) 5, 4 (2015), 1–19. [9] Robert A Jacobs, Michael I Jordan, Steven J Nowlan, and Geoffrey E Hinton. 1991. Adaptive mixtures of local experts. Neural computation 3, 1 (1991), 79–87. [10] Diederik P. Kingma and Jimmy Ba. 2015. Adam: A Method for Stochastic Optimization. In 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings, Yoshua Bengio and Yann LeCun (Eds.). http://arxiv.org/abs/1412.6980 [11] Jiaqi Ma, Zhe Zhao, Jilin Chen, Ang Li, Lichan Hong, and Ed H Chi. 2019. Snr: Sub-network routing for flexible parameter sharing in multi-task learning. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 33. 216–223. [12] Jiaqi Ma, Zhe Zhao, Xinyang Yi, Jilin Chen, Lichan Hong, and Ed H Chi. 2018. Modeling task relationships in multi-task learning with multi-gate mixture-ofexperts. In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. 1930–1939. [13] Xiao Ma, Liqin Zhao, Guan Huang, Zhi Wang, Zelin Hu, Xiaoqiang Zhu, and Kun Gai. 2018. Entire space multi-task model: An effective approach for estimating post-click conversion rate. In The 41st International ACM SIGIR Conference on Research & Development in Information Retrieval. 1137–1140. [14] Julian McAuley, Christopher Targett, Qinfeng Shi, and Anton Van Den Hengel. 2015. Image-based recommendations on styles and substitutes. In Proceedings of the 38th international ACM SIGIR conference on research and development in information retrieval. 43–52. [15] Ishan Misra, Abhinav Shrivastava, Abhinav Gupta, and Martial Hebert. 2016. Cross-stitch networks for multi-task learning. In Proceedings of the IEEE conference on computer vision and pattern recognition. 3994–4003. [16] Sebastian Ruder, Joachim Bingel, Isabelle Augenstein, and Anders Søgaard. 2019. Latent multi-task architecture learning. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 33. 4822–4829. [17] Hongyan Tang, Junning Liu, Ming Zhao, and Xudong Gong. 2020. Progressive layered extraction (ple): A novel multi-task learning (mtl) model for personalized recommendations. In Fourteenth ACM Conference on Recommender Systems. 269– 278. [18] Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 (2017). [19] Yining Wang, Liwei Wang, Yuanzhi Li, Di He, Wei Chen, and Tie-Yan Liu. 2013. A theoretical analysis of NDCG ranking measures. In Proceedings of the 26th annual conference on learning theory (COLT 2013), Vol. 8. 6. [20] Yuyan Wang, Zhe Zhao, Bo Dai, Christopher Fifty, Dong Lin, Lichan Hong, Li Wei, and Ed H Chi. 2022. Can Small Heads Help? Understanding and Improving Multi-Task Generalization. In Proceedings of the ACM Web Conference 2022. 3009– 3019. [21] Wikipedia. 2023. Pearson correlation coefficient — Wikipedia, The Free Encyclopedia. http://en.wikipedia.org/w/index.php?title=Pearson%20correlation% 20coefficient&oldid=1146097966. [Online; accessed 15-April-2023]. [22] Wikipedia contributors. 2022. Evaluation measures (information retrieval) — Wikipedia, The Free Encyclopedia. https://en.wikipedia.org/w/index.php?title= Evaluation_measures_(information_retrieval)&oldid=1095286224. [Online; accessed 9-January-2023]. [23] Wikipedia contributors. 2022. Mean squared error — Wikipedia, The Free Encyclopedia. https://en.wikipedia.org/w/index.php?title=Mean_squared_error& oldid=1127519968. [Online; accessed 31-January-2023]. [24] Dongbo Xi, Zhen Chen, Peng Yan, Yinger Zhang, Yongchun Zhu, Fuzhen Zhuang, and Yu Chen. 2021. Modeling the sequential dependence among audience multistep conversions with multi-task learning in targeted display advertising. In Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining. 3745–3755. [25] Yu Zhang and Qiang Yang. 2021. A survey on multi-task learning. IEEE Transactions on Knowledge and Data Engineering (2021). [26] Zhe Zhao, Lichan Hong, Li Wei, Jilin Chen, Aniruddh Nath, Shawn Andrews, Aditee Kumthekar, Maheswaran Sathiamoorthy, Xinyang Yi, and Ed Chi. 2019. Recommending what video to watch next: a multitask ranking system. In Proceedings of the 13th ACM Conference on Recommender Systems. 43–51. Authors:
(1) Yi Ren, Tencent, Beijing, China (yiren_bj@outlook.com);
(2) Ying Du, Tencent, Beijing, China (yingdu@tencent.com);
(3) Bin Wang, Tencent, Beijing, China (hillmwang@tencent.com);
(4) Shenzheng Zhang, Tencent, Beijing, China (qjzcyzhang@tencent.com). This paper is available on arxiv under CC BY 4.0 DEED license. Table of Links Abstract and 1 Introduction Abstract and 1 Introduction Methodology Experiments Conclusion and References Methodology Methodology Experiments Experiments Conclusion and References Conclusion and References 4. CONCLUSION In this paper, we propose the framework of D eep M utual L earning across task towers( DML ), which is compatible with various backbone multi-task networks. Extensive offline experiments help to verify DML’s effectiveness on multiple real-world datasets and across various base models. Moreover, thorough ablation studies are carried out to verify and understand the value of each newly introduced module. Finally, DML achieves significant online gains and has already been deployed to the online platform. D M L DML REFERENCES [1] Martín Abadi, Paul Barham, Jianmin Chen, Zhifeng Chen, Andy Davis, Jeffrey Dean, Matthieu Devin, Sanjay Ghemawat, Geoffrey Irving, Michael Isard, et al. 2016. Tensorflow: a system for large-scale machine learning.. In Osdi, Vol. 16. Savannah, GA, USA, 265–283. [2] Raman Arora, Amitabh Basu, Poorya Mianjy, and Anirbit Mukherjee. 2018. Understanding Deep Neural Networks with Rectified Linear Units. In 6th International Conference on Learning Representations, ICLR 2018, Vancouver, BC, Canada, April 30 - May 3, 2018, Conference Track Proceedings. OpenReview.net. https://openreview.net/forum?id=B1J_rgWRW [3] Rich Caruana. 1997. Multitask learning. Machine learning 28, 1 (1997), 41–75. [4] Paul Covington, Jay Adams, and Emre Sargin. 2016. Deep neural networks for youtube recommendations. In Proceedings of the 10th ACM conference on recommender systems. 191–198. [5] Ke Ding, Xin Dong, Yong He, Lei Cheng, Chilin Fu, Zhaoxin Huan, Hai Li, Tan Yan, Liang Zhang, Xiaolu Zhang, et al. 2021. MSSM: a multiple-level sparse sharing model for efficient multi-task learning. In Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval. 2237–2241. [6] Long Duong, Trevor Cohn, Steven Bird, and Paul Cook. 2015. Low resource dependency parsing: Cross-lingual parameter sharing in a neural network parser. In Proceedings of the 53rd annual meeting of the Association for Computational Linguistics and the 7th international joint conference on natural language processing (volume 2: short papers). 845–850. [7] Peter A Flach, José Hernández-Orallo, and Cèsar Ferri Ramirez. 2011. A coherent interpretation of AUC as a measure of aggregated classification performance. In ICML. [8] F Maxwell Harper and Joseph A Konstan. 2015. The movielens datasets: History and context. Acm transactions on interactive intelligent systems (tiis) 5, 4 (2015), 1–19. [9] Robert A Jacobs, Michael I Jordan, Steven J Nowlan, and Geoffrey E Hinton. 1991. Adaptive mixtures of local experts. Neural computation 3, 1 (1991), 79–87. [10] Diederik P. Kingma and Jimmy Ba. 2015. Adam: A Method for Stochastic Optimization. In 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings, Yoshua Bengio and Yann LeCun (Eds.). http://arxiv.org/abs/1412.6980 [11] Jiaqi Ma, Zhe Zhao, Jilin Chen, Ang Li, Lichan Hong, and Ed H Chi. 2019. Snr: Sub-network routing for flexible parameter sharing in multi-task learning. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 33. 216–223. [12] Jiaqi Ma, Zhe Zhao, Xinyang Yi, Jilin Chen, Lichan Hong, and Ed H Chi. 2018. Modeling task relationships in multi-task learning with multi-gate mixture-ofexperts. In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. 1930–1939. [13] Xiao Ma, Liqin Zhao, Guan Huang, Zhi Wang, Zelin Hu, Xiaoqiang Zhu, and Kun Gai. 2018. Entire space multi-task model: An effective approach for estimating post-click conversion rate. In The 41st International ACM SIGIR Conference on Research & Development in Information Retrieval. 1137–1140. [14] Julian McAuley, Christopher Targett, Qinfeng Shi, and Anton Van Den Hengel. 2015. Image-based recommendations on styles and substitutes. In Proceedings of the 38th international ACM SIGIR conference on research and development in information retrieval. 43–52. [15] Ishan Misra, Abhinav Shrivastava, Abhinav Gupta, and Martial Hebert. 2016. Cross-stitch networks for multi-task learning. In Proceedings of the IEEE conference on computer vision and pattern recognition. 3994–4003. [16] Sebastian Ruder, Joachim Bingel, Isabelle Augenstein, and Anders Søgaard. 2019. Latent multi-task architecture learning. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 33. 4822–4829. [17] Hongyan Tang, Junning Liu, Ming Zhao, and Xudong Gong. 2020. Progressive layered extraction (ple): A novel multi-task learning (mtl) model for personalized recommendations. In Fourteenth ACM Conference on Recommender Systems. 269– 278. [18] Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 (2017). [19] Yining Wang, Liwei Wang, Yuanzhi Li, Di He, Wei Chen, and Tie-Yan Liu. 2013. A theoretical analysis of NDCG ranking measures. In Proceedings of the 26th annual conference on learning theory (COLT 2013), Vol. 8. 6. [20] Yuyan Wang, Zhe Zhao, Bo Dai, Christopher Fifty, Dong Lin, Lichan Hong, Li Wei, and Ed H Chi. 2022. Can Small Heads Help? Understanding and Improving Multi-Task Generalization. In Proceedings of the ACM Web Conference 2022. 3009– 3019. [21] Wikipedia. 2023. Pearson correlation coefficient — Wikipedia, The Free Encyclopedia. http://en.wikipedia.org/w/index.php?title=Pearson%20correlation% 20coefficient&oldid=1146097966. [Online; accessed 15-April-2023]. [22] Wikipedia contributors. 2022. Evaluation measures (information retrieval) — Wikipedia, The Free Encyclopedia. https://en.wikipedia.org/w/index.php?title= Evaluation_measures_(information_retrieval)&oldid=1095286224. [Online; accessed 9-January-2023]. [23] Wikipedia contributors. 2022. Mean squared error — Wikipedia, The Free Encyclopedia. https://en.wikipedia.org/w/index.php?title=Mean_squared_error& oldid=1127519968. [Online; accessed 31-January-2023]. [24] Dongbo Xi, Zhen Chen, Peng Yan, Yinger Zhang, Yongchun Zhu, Fuzhen Zhuang, and Yu Chen. 2021. Modeling the sequential dependence among audience multistep conversions with multi-task learning in targeted display advertising. In Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining. 3745–3755. [25] Yu Zhang and Qiang Yang. 2021. A survey on multi-task learning. IEEE Transactions on Knowledge and Data Engineering (2021). [26] Zhe Zhao, Lichan Hong, Li Wei, Jilin Chen, Aniruddh Nath, Shawn Andrews, Aditee Kumthekar, Maheswaran Sathiamoorthy, Xinyang Yi, and Ed Chi. 2019. Recommending what video to watch next: a multitask ranking system. In Proceedings of the 13th ACM Conference on Recommender Systems. 43–51. Authors: (1) Yi Ren, Tencent, Beijing, China (yiren_bj@outlook.com); (2) Ying Du, Tencent, Beijing, China (yingdu@tencent.com); (3) Bin Wang, Tencent, Beijing, China (hillmwang@tencent.com); (4) Shenzheng Zhang, Tencent, Beijing, China (qjzcyzhang@tencent.com). Authors: Authors: (1) Yi Ren, Tencent, Beijing, China (yiren_bj@outlook.com); (2) Ying Du, Tencent, Beijing, China (yingdu@tencent.com); (3) Bin Wang, Tencent, Beijing, China (hillmwang@tencent.com); (4) Shenzheng Zhang, Tencent, Beijing, China (qjzcyzhang@tencent.com). This paper is available on arxiv under CC BY 4.0 DEED license. This paper is available on arxiv under CC BY 4.0 DEED license. available on arxiv available on arxiv

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DML Boosts Multi-Task Recommender Performance with Enhanced Models and Real-World Gains

New Framework for Deep Mutual Learning (DML) to Improve Multi-task Recommender Systems

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DML Revolutionizes Multi-Task Learning with Proven Effectiveness and Real-World Deployment

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