As the largest local retail & instant delivery platform in China, Meituan Waimai has deployed a personalized recommender system on server and recommend nearby stores to users through APP homepage. To capture real-time intention of users and flexibly adjust the recommendation results on the homepage, we further add an interactive recommender system. The existing interactive recommender systems in the industry mainly capture intention of users based on their feedback on a specific UI of questions. However, we find that it will undermine use fluency and increase use complexity by rashly inserting a new question UI when users browse the homepage. Therefore, we develop an Embedded Interactive Recommender System (EIRS) that directly infers users' intention according to their click behaviors on the homepage and dynamically inserts a new recommendation result into the homepage1. To demonstrate the effectiveness of EIRS, we conduct systematic online A/B Tests, where click-through & conversion rate of the inserted EIRS result is 132% higher than that of the initial result on the homepage, and the overall gross merchandise volume is effectively enhanced by 0.43%.

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[1]

Qingpeng Cai, Shuchang Liu, Xueliang Wang, Tianyou Zuo, Wentao Xie, Bin Yang, Dong Zheng, Peng Jiang, and Kun Gai. 2023. Reinforcing User Retention in a Billion Scale Short Video Recommender System. arXiv:2302.01724 [cs.LG]

Google Scholar

[2]

Konstantina Christakopoulou, Alex Beutel, Rui Li, Sagar Jain, and Ed H. Chi. 2018. Q&R: A Two-Stage Approach toward Interactive Recommendation. In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (London, United Kingdom) (KDD '18). Association for Computing Machinery, New York, NY, USA, 139--148. https://doi.org/10.1145/ 3219819.3219894

Digital Library

Google Scholar

[3]

Chongming Gao, Wenqiang Lei, Jiawei Chen, Shiqi Wang, Xiangnan He, Shijun Li, Biao Li, Yuan Zhang, and Peng Jiang. 2022. Cirs: Bursting filter bubbles by counterfactual interactive recommender system. arXiv preprint arXiv:2204.01266 (2022).

Google Scholar

[4]

Chongming Gao, Wenqiang Lei, Xiangnan He, Maarten de Rijke, and Tat-Seng Chua. 2021. Advances and challenges in conversational recommender systems: A survey. AI Open 2 (2021), 100--126. https://doi.org/10.1016/j.aiopen.2021.06.002

Crossref

Google Scholar

[5]

Po-Sen Huang, Xiaodong He, Jianfeng Gao, Li Deng, Alex Acero, and Larry Heck. 2013. Learning Deep Structured Semantic Models for Web Search Using Click- through Data (CIKM '13). Association for Computing Machinery, New York, NY, USA, 2333--2338. https://doi.org/10.1145/2505515.2505665

Digital Library

Google Scholar

[6]

Shijun Li, Wenqiang Lei, Qingyun Wu, Xiangnan He, Peng Jiang, and Tat-Seng Chua. 2021. Seamlessly Unifying Attributes and Items: Conversational Recommendation for Cold-Start Users. ACM Trans. Inf. Syst. 39, 4, Article 40 (aug 2021), 29 pages. https://doi.org/10.1145/3446427

Digital Library

Google Scholar

[7]

Jiaqi Ma, Zhe Zhao, Xinyang Yi, Jilin Chen, Lichan Hong, and Ed H Chi. 2018. Mod- eling task relationships in multi-task learning with multi-gate mixture-of-experts. In Proceedings of the 24th ACM SIGKDD international conference on knowledge discovery & data mining. 1930--1939.

Digital Library

Google Scholar

[8]

Yue Shi, Martha Larson, and Alan Hanjalic. 2014. Collaborative Filtering beyond the User-Item Matrix: A Survey of the State of the Art and Future Challenges. ACM Comput. Surv. 47, 1, Article 3 (may 2014), 45 pages. https://doi.org/10.1145/2556270

Digital Library

Google Scholar

[9]

Yu Zhu, Yu Gong, Qingwen Liu, Yingcai Ma, Wenwu Ou, Junxiong Zhu, Beidou Wang, Ziyu Guan, and Deng Cai. 2019. Query-based interactive recommenda- tion by meta-path and adapted attention-GRU. In Proceedings of the 28th ACM International Conference on Information and Knowledge Management. 2585--2593.

Google Scholar

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Zhan YNakajima TSakai TIshita EOhshima HHasibi FMao JJose J(2024)Effect of Presentation Methods on User Experiences and Perception in VR Shopping Recommender SystemsProceedings of the 2024 Annual International ACM SIGIR Conference on Research and Development in Information Retrieval in the Asia Pacific Region10.1145/3673791.3698430(241-248)Online publication date: 8-Dec-2024
https://dl.acm.org/doi/10.1145/3673791.3698430

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Interactive Recommendation System for Meituan Waimai
1. Information systems

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SIGIR '23: Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval

July 2023

3567 pages

ISBN:9781450394086

DOI:10.1145/3539618

General Chairs:
Hsin-Hsi Chen
National Taiwan University
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Wei-Jou (Edward) Duh
National Taiwan University
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Hen-Hsen Huang
Academia Sinica
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Program Chairs:
Makoto P. Kato
Spotify
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Josiane Mothe
Universite de Toulouse
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Barbara Poblete
University of Chile and Amazon Visiting Academic

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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Published: 18 July 2023

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Zhan YNakajima TSakai TIshita EOhshima HHasibi FMao JJose J(2024)Effect of Presentation Methods on User Experiences and Perception in VR Shopping Recommender SystemsProceedings of the 2024 Annual International ACM SIGIR Conference on Research and Development in Information Retrieval in the Asia Pacific Region10.1145/3673791.3698430(241-248)Online publication date: 8-Dec-2024
https://dl.acm.org/doi/10.1145/3673791.3698430

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