Xiaowen Shi
ABSTRACT
Re-ranking draws increased attention on both academics and industries, which rearranges the ranking list by modeling the mutual influence among items to better meet users' demands. Many existing re-ranking methods directly take the initial ranking list as input, and generate the optimal permutation through a well-designed context-wise model, which brings the evaluation-before-reranking problem. Meanwhile, evaluating all candidate permutations brings unacceptable computational costs in practice. Thus, to better balance efficiency and effectiveness, online systems usually use a two-stage architecture which uses some heuristic methods such as beam-search to generate a suitable amount of candidate permutations firstly, which are then fed into the evaluation model to get the optimal permutation. However, existing methods in both stages can be improved through the following aspects. As for generation stage, heuristic methods only use point-wise prediction scores and lack an effective judgment. As for evaluation stage, most existing context-wise evaluation models only consider the item context and lack more fine-grained feature context modeling.
This paper presents a novel end-to-end re-ranking framework named PIER to tackle the above challenges which still follows the two-stage architecture and contains two mainly modules named FPSM and OCPM. Inspired by long-time user behavior modeling methods, we apply SimHash in FPSM to select top-K candidates from the full permutation based on user's permutation-level interest in an efficient way. Then we design a novel omnidirectional attention mechanism in OCPM to better capture the context information in the permutation. Finally, we jointly train these two modules in an end-to-end way by introducing a comparative learning loss, which use the predict value of OCPM to guide the FPSM to generate better permutations. Offline experiment results demonstrate that PIER outperforms baseline models on both public and industrial datasets, and we have successfully deployed PIER on Meituan food delivery platform.
Supplemental Material
- Qingyao Ai, Keping Bi, Jiafeng Guo, and W Bruce Croft. 2018. Learning a deep listwise context model for ranking refinement. In The 41st international ACM SIGIR conference on research & development in information retrieval. 135--144.Google Scholar
Digital Library
- Areej Alsini, Du Q Huynh, and Amitava Datta. 2020. Hit ratio: An evaluation metric for hashtag recommendation. arXiv preprint arXiv:2010.01258 (2020).Google Scholar
- Irwan Bello, Sayali Kulkarni, Sagar Jain, Craig Boutilier, Ed Chi, Elad Eban, Xiyang Luo, Alan Mackey, and Ofer Meshi. 2018. Seq2slate: Re-ranking and slate optimization with rnns. arXiv preprint arXiv:1810.02019 (2018).Google Scholar
- Yue Cao, Xiaojiang Zhou, Jiaqi Feng, Peihao Huang, Yao Xiao, Dayao Chen, and Sheng Chen. 2022. Sampling Is All You Need on Modeling Long-Term User Behaviors for CTR Prediction. In Proceedings of the 31st ACM International Conference on Information & Knowledge Management. 2974--2983.Google ScholarDigital Library
- Moses S Charikar. 2002. Similarity estimation techniques from rounding algorithms. In Proceedings of the 34th annual ACM symposium on Theory of computing. 380--388.Google ScholarDigital Library
- Chi Chen, Hui Chen, Kangzhi Zhao, Junsheng Zhou, Li He, Hongbo Deng, Jian Xu, Bo Zheng, Yong Zhang, and Chunxiao Xing. 2022. EXTR: Click-Through Rate Prediction with Externalities in E-Commerce Sponsored Search. In Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. 2732--2740.Google ScholarDigital Library
- Dagui Chen, Qi Yan, Chunjie Chen, Zhenzhe Zheng, Yangsu Liu, Zhenjia Ma, Chuan Yu, Jian Xu, and Bo Zheng. 2022. Hierarchically Constrained Adaptive Ad Exposure in Feeds. In Proceedings of the 31st ACM International Conference on Information & Knowledge Management. 3003--3012.Google ScholarDigital Library
- Qiwei Chen, Changhua Pei, Shanshan Lv, Chao Li, Junfeng Ge, and Wenwu Ou. 2021. End-to-End User Behavior Retrieval in Click-Through Rate Prediction Model. arXiv preprint arXiv:2108.04468 (2021).Google Scholar
- Heng-Tze Cheng, Levent Koc, Jeremiah Harmsen, Tal Shaked, Tushar Chandra, Hrishi Aradhye, Glen Anderson, Greg Corrado, Wei Chai, Mustafa Ispir, et al. 2016. Wide & deep learning for recommender systems. In Proceedings of the 1st workshop on deep learning for recommender systems. 7--10.Google ScholarDigital Library
- 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.Google ScholarDigital Library
- Yufei Feng, Yu Gong, Fei Sun, Junfeng Ge, and Wenwu Ou. 2021. Revisit recommender system in the permutation prospective. arXiv preprint arXiv:2102.12057 (2021).Google Scholar
- Yufei Feng, Binbin Hu, Yu Gong, Fei Sun, Qingwen Liu, and Wenwu Ou. 2021. GRN: Generative Rerank Network for Context-wise Recommendation. arXiv preprint arXiv:2104.00860 (2021).Google Scholar
- Xudong Gong, Qinlin Feng, Yuan Zhang, Jiangling Qin, Weijie Ding, Biao Li, Peng Jiang, and Kun Gai. 2022. Real-time Short Video Recommendation on Mobile Devices. In Proceedings of the 31st ACM International Conference on Information & Knowledge Management. 3103--3112.Google ScholarDigital Library
- Huifeng Guo, Ruiming Tang, Yunming Ye, Zhenguo Li, and Xiuqiang He. 2017. DeepFM: a factorization-machine based neural network for CTR prediction. In Proceedings of the 26th International Joint Conference on Artificial Intelligence. 1725--1731.Google ScholarCross Ref
- Xiang Li, Shuwei Chen, Jian Dong, Jin Zhang, Yongkang Wang, Xingxing Wang, and Dong Wang. 2023. Decision-Making Context Interaction Network for Click-Through Rate Prediction. arXiv preprint arXiv:2301.12402 (2023).Google Scholar
- Jianxun Lian, Xiaohuan Zhou, Fuzheng Zhang, Zhongxia Chen, Xing Xie, and Guangzhong Sun. 2018. xdeepfm: Combining explicit and implicit feature interactions for recommender systems. In Proceedings of the 24th ACM SIGKDD international conference on knowledge discovery & data mining. 1754--1763.Google ScholarDigital Library
- Guogang Liao, Xuejian Li, Ze Wang, Fan Yang, Muzhi Guan, Bingqi Zhu, Yongkang Wang, Xingxing Wang, and Dong Wang. 2022. NMA: Neural Multi-slot Auctions with Externalities for Online Advertising. arXiv preprint arXiv:2205.10018 (2022).Google Scholar
- Guogang Liao, Ze Wang, Xiaoxu Wu, Xiaowen Shi, Chuheng Zhang, Yongkang Wang, Xingxing Wang, and Dong Wang. 2022. Cross dqn: Cross deep q network for ads allocation in feed. In Proceedings of the ACM Web Conference 2022. 401--409.Google ScholarDigital Library
- Gurmeet Singh Manku, Arvind Jain, and Anish Das Sarma. 2007. Detecting Near-Duplicates for Web Crawling. In Proceedings of the 16th International Conference on World Wide Web. https://doi.org/10.1145/1242572.1242592Google ScholarDigital Library
- Changhua Pei, Yi Zhang, Yongfeng Zhang, Fei Sun, Xiao Lin, Hanxiao Sun, Jian Wu, Peng Jiang, Junfeng Ge, Wenwu Ou, et al. 2019. Personalized re-ranking for recommendation. In Proceedings of the 13th ACM conference on recommender systems. 3--11.Google ScholarDigital Library
- D Raj Reddy et al. 1977. Speech understanding systems: A summary of results of the five-year research effort. Department of Computer Science. Camegie-Mell University, Pittsburgh, PA 17 (1977), 138.Google Scholar
- 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).Google Scholar
- Ruoxi Wang, Bin Fu, Gang Fu, and Mingliang Wang. 2017. Deep & cross network for ad click predictions. In Proceedings of the ADKDD'17. 1--7.Google ScholarDigital Library
- Zhe Wang, Liqin Zhao, Biye Jiang, Guorui Zhou, Xiaoqiang Zhu, and Kun Gai. 2020. Cold: Towards the next generation of pre-ranking system. arXiv preprint arXiv:2007.16122 (2020).Google Scholar
- Yunjia Xi, Weiwen Liu, Xinyi Dai, Ruiming Tang, Weinan Zhang, Qing Liu, Xiuqiang He, and Yong Yu. 2021. Context-aware reranking with utility maximization for recommendation. arXiv preprint arXiv:2110.09059 (2021).Google Scholar
- Guorui Zhou, Na Mou, Ying Fan, Qi Pi, Weijie Bian, Chang Zhou, Xiaoqiang Zhu, and Kun Gai. 2019. Deep interest evolution network for click-through rate prediction. In Proceedings of the AAAI conference on artificial intelligence, Vol. 33. 5941--5948.Google ScholarDigital Library
- Guorui Zhou, Xiaoqiang Zhu, Chenru Song, Ying Fan, Han Zhu, Xiao Ma, Yanghui Yan, Junqi Jin, Han Li, and Kun Gai. 2018. Deep interest network for click-through rate prediction. In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. 1059--1068.Google ScholarDigital Library
- Han Zhu, Xiang Li, Pengye Zhang, Guozheng Li, Jie He, Han Li, and Kun Gai. 2018. Learning tree-based deep model for recommender systems. In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. 1079--1088.Google ScholarDigital Library
- Tao Zhuang, Wenwu Ou, and Zhirong Wang. 2018. Globally optimized mutual influence aware ranking in e-commerce search. In Proceedings of the 27th International Joint Conference on Artificial Intelligence. 3725--3731.Google ScholarCross Ref
Index Terms
- PIER: Permutation-Level Interest-Based End-to-End Re-ranking Framework in E-commerce
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