Wei Guo
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- CIKM '22: Proceedings of the 31st ACM International Conference on Information & Knowledge Management (3)
- CIKM '23: Proceedings of the 32nd ACM International Conference on Information and Knowledge Management (3)
- CIKM '20: Proceedings of the 29th ACM International Conference on Information & Knowledge Management (2)
- KDD '23: Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (2)
- SIGIR '20: Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval (2)
- CIKM '21: Proceedings of the 30th ACM International Conference on Information & Knowledge Management (1)
- CIKM '24: Proceedings of the 33rd ACM International Conference on Information and Knowledge Management (1)
- KDD '20: Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (1)
- KDD '21: Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining (1)
- KDD '24: Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (1)
- RecSys '23: Proceedings of the 17th ACM Conference on Recommender Systems (1)
- RecSysChallenge '22: Proceedings of the Recommender Systems Challenge 2022 (1)
- SIGIR '21: Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval (1)
- SIGIR '22: Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval (1)
- SIGIR'19: Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval (1)
- WSDM '24: Proceedings of the 17th ACM International Conference on Web Search and Data Mining (1)
- WWW '20: Companion Proceedings of the Web Conference 2020 (1)
- WWW '22: Proceedings of the ACM Web Conference 2022 (1)
- WWW '23: Proceedings of the ACM Web Conference 2023 (1)
- WWW '24: Proceedings of the ACM Web Conference 2024 (1)
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- research-articlePublished By ACMPublished By ACM
Enhancing Click-through Rate Prediction in Recommendation Domain with Search Query Representation
- Yuening Wang
Huawei Noah's Ark Lab, Markham, Canada
, - Man Chen
Huawei Noah's Ark Lab, Montreal, Canada
, - Yaochen Hu
Huawei Noah's Ark Lab, Montreal, Canada
, - Wei Guo
Huawei Noah's Ark Lab, Singapore, Singapore
, - Yingxue Zhang
Huawei Noah's Ark Lab, Markham, Canada
, - Huifeng Guo
Huawei Noah's Ark Lab, Shenzhen, China
, - Yong Liu
Huawei Noah's Ark Lab, Singapore, Singapore
, - Mark Coates
McGill University, Montreal, Canada
CIKM '24: Proceedings of the 33rd ACM International Conference on Information and Knowledge Management•October 2024, pp 2462-2471• https://doi.org/10.1145/3627673.3679849Many platforms, such as e-commerce websites, offer both search and recommendation services simultaneously to better meet users' diverse needs. Recommendation services suggest items based on user preferences, while search services allow users to search ...
- 0Citation
- 122
- Downloads
MetricsTotal Citations0Total Downloads122Last 12 Months122Last 6 weeks30
- Yuening Wang
- research-articlePublished By ACMPublished By ACM
Dataset Regeneration for Sequential Recommendation
- Mingjia Yin
University of Science and Technology of China & State Key Laboratory of Cognitive Intelligence, Hefei, China
, - Hao Wang
University of Science and Technology of China & State Key Laboratory of Cognitive Intelligence, Hefei, China
, - Wei Guo
Huawei Singapore Research Center, Singapore, Singapore
, - Yong Liu
Huawei Singapore Research Center, Singapore, Singapore
, - Suojuan Zhang
University of Science and Technology of China & State Key Laboratory of Cognitive Intelligence, Hefei, China
, - Sirui Zhao
University of Science and Technology of China & State Key Laboratory of Cognitive Intelligence, Hefei, China
, - Defu Lian
University of Science and Technology of China & State Key Laboratory of Cognitive Intelligence, Hefei, China
, - Enhong Chen
University of Science and Technology of China & State Key Laboratory of Cognitive Intelligence, Hefei, China
KDD '24: Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining•August 2024, pp 3954-3965• https://doi.org/10.1145/3637528.3671841The sequential recommender (SR) system is a crucial component of modern recommender systems, as it aims to capture the evolving preferences of users. Significant efforts have been made to enhance the capabilities of SR systems. These methods typically ...
- 1Citation
- 716
- Downloads
MetricsTotal Citations1Total Downloads716Last 12 Months716Last 6 weeks121- 1
Supplementary Materialrtfp0953-video.mp4
- Mingjia Yin
- research-articlePublished By ACMPublished By ACM
Efficient Noise-Decoupling for Multi-Behavior Sequential Recommendation
- Yongqiang Han
University of Science and Technology of China & State Key Laboratory of Cognitive Intelligence, Hefei, China
, - Hao Wang
University of Science and Technology of China & State Key Laboratory of Cognitive Intelligence, Hefei, China
, - Kefan Wang
University of Science and Technology of China & State Key Laboratory of Cognitive Intelligence, Hefei, China
, - Likang Wu
University of Science and Technology of China & State Key Laboratory of Cognitive Intelligence, Hefei, China
, - Zhi Li
Shenzhen International Graduate School, Tsinghua University, Shenzhen, China
, - Wei Guo
Huawei Singapore Research Center, Singapore, Singapore
, - Yong Liu
Huawei Singapore Research Center, Singapore, Singapore
, - Defu Lian
University of Science and Technology of China & State Key Laboratory of Cognitive Intelligence, Hefei, China
, - Enhong Chen
University of Science and Technology of China & State Key Laboratory of Cognitive Intelligence, Hefei, China
WWW '24: Proceedings of the ACM Web Conference 2024•May 2024, pp 3297-3306• https://doi.org/10.1145/3589334.3645380In recommendation systems, users frequently engage in multiple types of behaviors, such as clicking, adding to cart, and purchasing. Multi-behavior sequential recommendation aims to jointly consider multiple behaviors to improve the target behavior's ...
- 2Citation
- 427
- Downloads
MetricsTotal Citations2Total Downloads427Last 12 Months427Last 6 weeks81- 1
Supplementary Materialrfp0395.mp4
- Yongqiang Han
- research-articleOpen AccessPublished By ACMPublished By ACM
User Behavior Enriched Temporal Knowledge Graphs for Sequential Recommendation
- Hengchang Hu
National University of Singapore, Singapore, Singapore
, - Wei Guo
Huawei Noah's Ark Lab, Singapore, Singapore
, - Xu Liu
National University of Singapore, Singapore, Singapore
, - Yong Liu
Huawei Noah's Ark Lab, Singapore, Singapore
, - Ruiming Tang
Huawei Noah's Ark Lab, Shenzhen, China
, - Rui Zhang
ruizhang.info, Beijing, China
, - Min-Yen Kan
National University of Singapore, Singapore, Singapore
WSDM '24: Proceedings of the 17th ACM International Conference on Web Search and Data Mining•March 2024, pp 266-275• https://doi.org/10.1145/3616855.3635762Knowledge Graphs (KGs) enhance recommendations by providing external connectivity between items. However, there is limited research on distilling relevant knowledge in sequential recommendation, where item connections can change over time. To address ...
- 0Citation
- 1,078
- Downloads
MetricsTotal Citations0Total Downloads1,078Last 12 Months1,078Last 6 weeks202
- Hengchang Hu
- research-articlePublished By ACMPublished By ACM
DFFM: Domain Facilitated Feature Modeling for CTR Prediction
- Wei Guo
Huawei Noah's Ark Lab, Huawei, Shanghai, China
, - Chenxu Zhu
Huawei Noah's Ark Lab, Huawei, Shanghai, China
, - Fan Yan
Huawei Noah's Ark Lab, Huawei, Shanghai, China
, - Bo Chen
Huawei Noah's Ark Lab, Shanghai, China
, - Weiwen Liu
Huawei Noah's Ark Lab, Huawei, Shanghai, China
, - Huifeng Guo
Huawei Noah's Ark Lab, Huawei, Shanghai, China
, - Hongkun Zheng
Huawei Technologies Co Ltd, Shenzhen, China
, - Yong Liu
Huawei Noah's Ark Lab, Huawei, Shanghai, China
, - Ruiming Tang
Huawei Noah's Ark Lab, Huawei, Shanghai, China
CIKM '23: Proceedings of the 32nd ACM International Conference on Information and Knowledge Management•October 2023, pp 4602-4608• https://doi.org/10.1145/3583780.3615469CTR prediction is critical to industrial recommender systems. Recently, with the growth of business domains in enterprises, much attention has been focused on the multi-domain CTR recommendation. Numerous models have been proposed that attempt to use a ...
- 4Citation
- 542
- Downloads
MetricsTotal Citations4Total Downloads542Last 12 Months359Last 6 weeks33
- Wei Guo
- research-articlePublished By ACMPublished By ACM
APGL4SR: A Generic Framework with Adaptive and Personalized Global Collaborative Information in Sequential Recommendation
- Mingjia Yin
University of Science and Technology of China & State Key Laboratory of Cognitive Intelligence, Hefei, China
, - Hao Wang
University of Science and Technology of China & State Key Laboratory of Cognitive Intelligence, Hefei, China
, - Xiang Xu
University of Science and Technology of China & State Key Laboratory of Cognitive Intelligence, Hefei, China
, - Likang Wu
University of Science and Technology of China & State Key Laboratory of Cognitive Intelligence, Hefei, China
, - Sirui Zhao
University of Science and Technology of China & State Key Laboratory of Cognitive Intelligence, Hefei, China
, - Wei Guo
Huawei Singapore Research Center, Singapore, Singapore
, - Yong Liu
Huawei Singapore Research Center, Singapore, Singapore
, - Ruiming Tang
Huawei Noah's Ark Lab, Shenzhen, China
, - Defu Lian
University of Science and Technology of China & State Key Laboratory of Cognitive Intelligence, Hefei, China
, - Enhong Chen
University of Science and Technology of China & State Key Laboratory of Cognitive Intelligence, Hefei, China
CIKM '23: Proceedings of the 32nd ACM International Conference on Information and Knowledge Management•October 2023, pp 3009-3019• https://doi.org/10.1145/3583780.3614781The sequential recommendation system has been widely studied for its promising effectiveness in capturing dynamic preferences buried in users' sequential behaviors. Despite the considerable achievements, existing methods usually focus on intra-sequence ...
- 6Citation
- 325
- Downloads
MetricsTotal Citations6Total Downloads325Last 12 Months183Last 6 weeks20
- Mingjia Yin
- research-articleOpen AccessPublished By ACMPublished By ACM
Adaptive Multi-Modalities Fusion in Sequential Recommendation Systems
- Hengchang Hu
National University of Singapore, Singapore, Singapore
, - Wei Guo
Huawei Noah's Ark Lab, Singapore, Singapore
, - Yong Liu
Huawei Noah's Ark Lab, Singapore, Singapore
, - Min-Yen Kan
National University of Singapore, Singapore, Singapore
CIKM '23: Proceedings of the 32nd ACM International Conference on Information and Knowledge Management•October 2023, pp 843-853• https://doi.org/10.1145/3583780.3614775In sequential recommendation, multi-modal information (e.g., text or image) can provide a more comprehensive view of an item's profile. The optimal stage (early or late) to fuse modality features into item representations is still debated. We propose a ...
- 11Citation
- 1,640
- Downloads
MetricsTotal Citations11Total Downloads1,640Last 12 Months1,346Last 6 weeks135- 1
Supplementary Materialfull0584-video.mp4
- Hengchang Hu
- tutorialPublished By ACMPublished By ACM
User Behavior Modeling with Deep Learning for Recommendation: Recent Advances
- Weiwen Liu
Huawei Noah's Ark Lab, China
, - Wei Guo
Huawei Noah's Ark Lab, Singapore
, - Yong Liu
Huawei Noah's Ark Lab, Singapore
, - Ruiming Tang
Huawei Noah's Ark Lab, China
, - Hao Wang
University of Science and Technology of China, China
RecSys '23: Proceedings of the 17th ACM Conference on Recommender Systems•September 2023, pp 1286-1287• https://doi.org/10.1145/3604915.3609496User Behavior Modeling (UBM) plays a critical role in user interest learning, and has been extensively used in recommender systems. The exploration of key interactive patterns between users and items has yielded significant improvements and great ...
- 3Citation
- 303
- Downloads
MetricsTotal Citations3Total Downloads303Last 12 Months154Last 6 weeks23
- Weiwen Liu
- research-article
A survey on user behavior modeling in recommender systems
- Zhicheng He
Huawei Noah's Ark Lab, Shenzhen, China
, - Weiwen Liu
Huawei Noah's Ark Lab, Shenzhen, China
, - Wei Guo
Huawei Noah's Ark Lab, Singapore
, - Jiarui Qin
Shanghai Jiao Tong University, Shanghai, China
, - Yingxue Zhang
Huawei Noah's Ark Lab, Montreal, Canada
, - Yaochen Hu
Huawei Noah's Ark Lab, Montreal, Canada
, - Ruiming Tang
Huawei Noah's Ark Lab, Shenzhen, China
IJCAI '23: Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence•August 2023, Article No.: 746, pp 6656-6664• https://doi.org/10.24963/ijcai.2023/746User Behavior Modeling (UBM) plays a critical role in user interest learning, which has been extensively used in recommender systems. Crucial interactive patterns between users and items have been exploited, which brings compelling improvements in many ...
- 1Citation
MetricsTotal Citations1
- Zhicheng He
- research-articleOpen AccessPublished By ACMPublished By ACM
Coarse-to-Fine Knowledge-Enhanced Multi-Interest Learning Framework for Multi-Behavior Recommendation
- Chang Meng
Shenzhen International Graduate School, Tsinghua University, China
, - Ziqi Zhao
School of Computer Science, Beihang University, China
, - Wei Guo
Huawei Singapore Research Center, Singapore
, - Yingxue Zhang
Huawei Technologies Canada, Canada
, - Haolun Wu
McGill University, Canada
, - Chen Gao
Department of Electronic Engineering, Tsinghua University, China
, - Dong Li
Huawei Noah’s Ark Lab, China
, - Xiu Li
Shenzhen International Graduate School, Tsinghua University, China
, - Ruiming Tang
Huawei Noah’s Ark Lab, China
ACM Transactions on Information Systems, Volume 42, Issue 1•January 2024, Article No.: 30, pp 1-27 • https://doi.org/10.1145/3606369Multi-types of behaviors (e.g., clicking, carting, purchasing, etc.) widely exist in most real-world recommendation scenarios, which are beneficial to learn users’ multi-faceted preferences. As dependencies are explicitly exhibited by the multiple types ...
- 10Citation
- 3,269
- Downloads
MetricsTotal Citations10Total Downloads3,269Last 12 Months2,219Last 6 weeks205
- Chang Meng
- research-articleOpen AccessPublished By ACMPublished By ACM
Hierarchical Projection Enhanced Multi-behavior Recommendation
- Chang Meng
Tsinghua University, Shenzhen, China
, - Hengyu Zhang
Tsinghua University, Shenzhen, China
, - Wei Guo
Huawei Singapore Research Center, Singapore, Singapore
, - Huifeng Guo
Huawei Noah's Ark Lab, Shenzhen, China
, - Haotian Liu
Tsinghua University, Shenzhen, China
, - Yingxue Zhang
Huawei Technologies Canada, Montreal, Canada
, - Hongkun Zheng
Huawei Technologies Co Ltd, Shenzhen, China
, - Ruiming Tang
Huawei Noah's Ark Lab, Shenzhen, China
, - Xiu Li
Tsinghua University, Shenzhen, China
, - Rui Zhang
ruizhang.info, Shenzhen, China
KDD '23: Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining•August 2023, pp 4649-4660• https://doi.org/10.1145/3580305.3599838Various types of user behaviors are recorded in most real-world recommendation scenarios. To fully utilize the multi-behavior information, the exploration of multiplex interaction among them is essential. Many multi-task learning based multi-behavior ...
- 12Citation
- 1,961
- Downloads
MetricsTotal Citations12Total Downloads1,961Last 12 Months1,219Last 6 weeks110- 1
Supplementary Materialadfp194-2min-promo.mp4
- Chang Meng
- research-articlefreePublished By ACMPublished By ACM
MAP: A Model-agnostic Pretraining Framework for Click-through Rate Prediction
- Jianghao Lin
Shanghai Jiao Tong University, Shanghai, China
, - Yanru Qu
Shanghai Jiao Tong University, Shanghai, China
, - Wei Guo
Huawei Noah's Ark Lab, Shenzhen, China
, - Xinyi Dai
Shanghai Jiao Tong University, Shanghai, China
, - Ruiming Tang
Huawei Noah's Ark Lab, Shenzhen, China
, - Yong Yu
Shanghai Jiao Tong University, Shanghai, China
, - Weinan Zhang
Shanghai Jiao Tong University, Shanghai, China
KDD '23: Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining•August 2023, pp 1384-1395• https://doi.org/10.1145/3580305.3599422With the widespread application of online advertising systems, click-through rate (CTR) prediction has received more and more attention and research. The most prominent features of CTR prediction are its multi-field categorical data format, and vast and ...
- 14Citation
- 681
- Downloads
MetricsTotal Citations14Total Downloads681Last 12 Months351Last 6 weeks35- 2
- Jianghao Lin
- research-articleOpen AccessPublished By ACMPublished By ACM
Compressed Interaction Graph based Framework for Multi-behavior Recommendation
- Wei Guo
Huawei Noah's Ark Lab, China
, - Chang Meng
Shenzhen International Graduate School, Tsinghua University, China
, - Enming Yuan
Institute for Interdisciplinary Information Sciences, Tsinghua University, China
, - Zhicheng He
Huawei Noah's Ark Lab, China
, - Huifeng Guo
Huawei Noah's Ark Lab, China
, - Yingxue Zhang
Huawei Technologies, Canada
, - Bo Chen
Huawei Noah's Ark Lab, China
, - Yaochen Hu
Huawei Technologies, Canada
, - Ruiming Tang
Huawei Noah's Ark Lab, China
, - Xiu Li
Shenzhen International Graduate School, Tsinghua University, China
, - Rui Zhang
ruizhang.info, China
WWW '23: Proceedings of the ACM Web Conference 2023•April 2023, pp 960-970• https://doi.org/10.1145/3543507.3583312Multi-types of user behavior data (e.g., clicking, adding to cart, and purchasing) are recorded in most real-world recommendation scenarios, which can help to learn users’ multi-faceted preferences. However, it is challenging to explore multi-behavior ...
- 10Citation
- 665
- Downloads
MetricsTotal Citations10Total Downloads665Last 12 Months409Last 6 weeks76
- Wei Guo
- research-articleOpen AccessPublished By ACMPublished By ACM
Disentangling Past-Future Modeling in Sequential Recommendation via Dual Networks
- Hengyu Zhang
Tsinghua University, Shenzhen, China
, - Enming Yuan
Tsinghua University, Beijing, China
, - Wei Guo
Huawei Noah's Ark Lab, Shenzhen, China
, - Zhicheng He
Huawei Noah's Ark Lab, Shenzhen, China
, - Jiarui Qin
Shanghai Jiao Tong University, Shanghai, China
, - Huifeng Guo
Huawei Noah's Ark Lab, Shenzhen, China
, - Bo Chen
Huawei Noah's Ark Lab, Shenzhen, China
, - Xiu Li
Tsinghua University, Shenzhen, China
, - Ruiming Tang
Huawei Noah's Ark Lab, Shenzhen, China
CIKM '22: Proceedings of the 31st ACM International Conference on Information & Knowledge Management•October 2022, pp 2549-2558• https://doi.org/10.1145/3511808.3557289Sequential recommendation (SR) plays an important role in personalized recommender systems because it captures dynamic and diverse preferences from users' real-time increasing behaviors. Unlike the standard autoregressive training strategy, future data (...
- 7Citation
- 855
- Downloads
MetricsTotal Citations7Total Downloads855Last 12 Months248Last 6 weeks22- 1
Supplementary MaterialCIKM22-fp0217.mp4
- Hengyu Zhang
- research-articlePublished By ACMPublished By ACM
Numerical Feature Representation with Hybrid N-ary Encoding
- Bo Chen
Huawei Noah's Ark Lab, Shenzhen, China
, - Huifeng Guo
Huawei Noah's Ark Lab, Shenzhen, China
, - Weiwen Liu
Huawei Noah's Ark Lab, Shenzhen, China
, - Yue Ding
Shanghai Jiao Tong University, Shanghai, China
, - Yunzhe Li
Shanghai Jiao Tong University, Shanghai, China
, - Wei Guo
Huawei Noah's Ark Lab, Shenzhen, China
, - Yichao Wang
Huawei Noah's Ark Lab, Shenzhen, China
, - Zhicheng He
Huawei Noah's Ark Lab, Shenzhen, China
, - Ruiming Tang
Huawei Noah's Ark Lab, Shenzhen, China
, - Rui Zhang
ruizhang.info, Shenzhen, China
CIKM '22: Proceedings of the 31st ACM International Conference on Information & Knowledge Management•October 2022, pp 2984-2993• https://doi.org/10.1145/3511808.3557090Numerical features (e.g., statistical features) are widely used in recommender systems and online advertising. Existing approaches for numerical feature representation in industry are primarily based on discretization. However, hard-discretization based ...
- 1Citation
- 267
- Downloads
MetricsTotal Citations1Total Downloads267Last 12 Months57Last 6 weeks4- 1
Supplementary MaterialCIKM22-app080.mp4
- Bo Chen
- research-articlePublished By ACMPublished By ACM
IntTower: The Next Generation of Two-Tower Model for Pre-Ranking System
- Xiangyang Li
Peking University, Beijing, China
, - Bo Chen
Huawei Noah's Ark Lab, Shenzhen, China
, - Huifeng Guo
Huawei Noah's Ark Lab, Shenzhen, China
, - Jingjie Li
Huawei Noah's Ark Lab, Shenzhen, China
, - Chenxu Zhu
Shanghai JiaoTong University, Shanghai, China
, - Xiang Long
Beijing University of Posts and Telecommunication, Beijing, China
, - Sujian Li
Peking University, Beijing, China
, - Yichao Wang
Huawei Noah's Ark Lab, Shenzhen, China
, - Wei Guo
Huawei Noah's Ark Lab, Shenzhen, China
, - Longxia Mao
Huawei Technologies Co Ltd, Shenzhen, China
, - Jinxing Liu
Huawei Technologies Co Ltd, Shenzhen, China
, - Zhenhua Dong
Huawei Noah's Ark Lab, Shenzhen, China
, - Ruiming Tang
Huawei Noah's Ark Lab, Shenzhen, China
CIKM '22: Proceedings of the 31st ACM International Conference on Information & Knowledge Management•October 2022, pp 3292-3301• https://doi.org/10.1145/3511808.3557072Scoring a large number of candidates precisely in several milliseconds is vital for industrial pre-ranking systems. Existing pre-ranking systems primarily adopt the two-tower model since the "user-item decoupling architecture" paradigm is able to balance ...
- 8Citation
- 352
- Downloads
MetricsTotal Citations8Total Downloads352Last 12 Months106Last 6 weeks30
- Xiangyang Li
- research-articlePublished By ACMPublished By ACM
Fashion Recommendation with a real Recommender System Flow
- Qi Zhang
Noah's Ark Lab, Huawei, China and VMALL, Huawei Technologies Co Ltd, China
, - Guohao Cai
Noah's Ark Lab, Huawei, China
, - Wei Guo
Noah's Ark Lab, Huawei, China
, - Yi Han
Chinese University of Hong Kong, China
, - Zhenhua Dong
Noah's Ark Lab, Huawei, China
, - Ruiming Tang
Noah's Ark Lab, Huawei, China
, - Liangbi Li
VMALL, Huawei Technologies Co Ltd, China
RecSysChallenge '22: Proceedings of the Recommender Systems Challenge 2022•September 2022, pp 4-9• https://doi.org/10.1145/3556702.3556792In this technical report, we present our solution of RecSys Challenge 2022 focusing on the fashion recommendation. We produce recommendations in two steps: (i) the retrieval step, which generates a candidate item set based on multiple cheap-to-compute ...
- 0Citation
- 235
- Downloads
MetricsTotal Citations0Total Downloads235Last 12 Months27Last 6 weeks2
- Qi Zhang
- research-articleOpen AccessPublished By ACMPublished By ACM
Multi-Behavior Sequential Transformer Recommender
- Enming Yuan
Tsinghua University, Beijing, China
, - Wei Guo
Noah's Ark Lab, Huawei, Shenzhen, China
, - Zhicheng He
Noah's Ark Lab, Huawei, Shenzhen, China
, - Huifeng Guo
Noah's Ark Lab, Huawei, Shenzhen, China
, - Chengkai Liu
Shanghai Jiao Tong University, Shanghai, China
, - Ruiming Tang
Noah's Ark Lab, Huawei, Shenzhen, China
SIGIR '22: Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval•July 2022, pp 1642-1652• https://doi.org/10.1145/3477495.3532023In most real-world recommender systems, users interact with items in a sequential and multi-behavioral manner. Exploring the fine-grained relationship of items behind the users' multi-behavior interactions is critical in improving the performance of ...
- 37Citation
- 4,624
- Downloads
MetricsTotal Citations37Total Downloads4,624Last 12 Months2,151Last 6 weeks341- 1
Supplementary MaterialSIGIR22-fp0855.MP4
- Enming Yuan
- research-articlePublished By ACMPublished By ACM
Cross Pairwise Ranking for Unbiased Item Recommendation
- Qi Wan
University of Science and Technology of China, China
, - Xiangnan He
University of Science and Technology of China, China
, - Xiang Wang
University of Science and Technology of China, China
, - Jiancan Wu
University of Science and Technology of China, China
, - Wei Guo
Huawei Noah's Ark Lab, China
, - Ruiming Tang
Huawei Noah's Ark Lab, China
WWW '22: Proceedings of the ACM Web Conference 2022•April 2022, pp 2370-2378• https://doi.org/10.1145/3485447.3512010Most recommender systems optimize the model on observed interaction data, which is affected by the previous exposure mechanism and exhibits many biases like popularity bias. The loss functions, such as the mostly used pointwise Binary Cross-Entropy and ...
- 24Citation
- 771
- Downloads
MetricsTotal Citations24Total Downloads771Last 12 Months82Last 6 weeks7
- Qi Wan
- research-articlePublished By ACMPublished By ACM
Enhancing Explicit and Implicit Feature Interactions via Information Sharing for Parallel Deep CTR Models
- Bo Chen
Huawei Noah's Ark Lab, Shenzhen, China
, - Yichao Wang
Huawei Noah's Ark Lab, Shenzhen, China
, - Zhirong Liu
Huawei Noah's Ark Lab, Shenzhen, China
, - Ruiming Tang
Huawei Noah's Ark Lab, Shenzhen, China
, - Wei Guo
Huawei Noah's Ark Lab, Shenzhen, China
, - Hongkun Zheng
Huawei Technologies Co Ltd, Shenzhen, China
, - Weiwei Yao
Huawei Technologies Co Ltd, Shenzhen, China
, - Muyu Zhang
Huawei Technologies Co Ltd, Shenzhen, China
, - Xiuqiang He
Huawei Noah's Ark Lab, Shenzhen, China
CIKM '21: Proceedings of the 30th ACM International Conference on Information & Knowledge Management•October 2021, pp 3757-3766• https://doi.org/10.1145/3459637.3481915Effectively modeling feature interactions is crucial for CTR prediction in industrial recommender systems. The state-of-the-art deep CTR models with parallel structure (e.g., DCN) learn explicit and implicit feature interactions through independent ...
- 22Citation
- 558
- Downloads
MetricsTotal Citations22Total Downloads558Last 12 Months100Last 6 weeks6
- Bo Chen
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These include:- co-authors: if we have two names and cannot disambiguate them based on name alone, then we see if they have a co-author in common. If so, this weighs towards the two names being the same person.
- affiliations: names in common with same affiliation weighs toward the two names being the same person.
- publication title: names in common whose works are published in same journal weighs toward the two names being the same person.
- keywords: names in common whose works address the same subject matter as determined from title and keywords, weigh toward being the same person.
The more conservative the merging algorithms, the more bits of evidence are required before a merge is made, resulting in greater precision but lower recall of works for a given Author Profile. Many bibliographic records have only author initials. Many names lack affiliations. With very common family names, typical in Asia, more liberal algorithms result in mistaken merges.
Automatic normalization of author names is not exact. Hence it is clear that manual intervention based on human knowledge is required to perfect algorithmic results. ACM is meeting this challenge, continuing to work to improve the automated merges by tweaking the weighting of the evidence in light of experience.
- Bibliometrics: In 1926, Alfred Lotka formulated his power law (known as Lotka's Law) describing the frequency of publication by authors in a given field. According to this bibliometric law of scientific productivity, only a very small percentage (~6%) of authors in a field will produce more than 10 articles while the majority (perhaps 60%) will have but a single article published. With ACM's first cut at author name normalization in place, the distribution of our authors with 1, 2, 3..n publications does not match Lotka's Law precisely, but neither is the distribution curve far off. For a definition of ACM's first set of publication statistics, see Bibliometrics
- Future Direction:
The initial release of the Author Edit Screen is open to anyone in the community with an ACM account, but it is limited to personal information. An author's photograph, a Home Page URL, and an email may be added, deleted or edited. Changes are reviewed before they are made available on the live site.
ACM will expand this edit facility to accommodate more types of data and facilitate ease of community participation with appropriate safeguards. In particular, authors or members of the community will be able to indicate works in their profile that do not belong there and merge others that do belong but are currently missing.
A direct search interface for Author Profiles will be built.
An institutional view of works emerging from their faculty and researchers will be provided along with a relevant set of metrics.
It is possible, too, that the Author Profile page may evolve to allow interested authors to upload unpublished professional materials to an area available for search and free educational use, but distinct from the ACM Digital Library proper. It is hard to predict what shape such an area for user-generated content may take, but it carries interesting potential for input from the community.
Bibliometrics
The ACM DL is a comprehensive repository of publications from the entire field of computing.
It is ACM's intention to make the derivation of any publication statistics it generates clear to the user.
- Average citations per article = The total Citation Count divided by the total Publication Count.
- Citation Count = cumulative total number of times all authored works by this author were cited by other works within ACM's bibliographic database. Almost all reference lists in articles published by ACM have been captured. References lists from other publishers are less well-represented in the database. Unresolved references are not included in the Citation Count. The Citation Count is citations TO any type of work, but the references counted are only FROM journal and proceedings articles. Reference lists from books, dissertations, and technical reports have not generally been captured in the database. (Citation Counts for individual works are displayed with the individual record listed on the Author Page.)
- Publication Count = all works of any genre within the universe of ACM's bibliographic database of computing literature of which this person was an author. Works where the person has role as editor, advisor, chair, etc. are listed on the page but are not part of the Publication Count.
- Publication Years = the span from the earliest year of publication on a work by this author to the most recent year of publication of a work by this author captured within the ACM bibliographic database of computing literature (The ACM Guide to Computing Literature, also known as "the Guide".
- Available for download = the total number of works by this author whose full texts may be downloaded from an ACM full-text article server. Downloads from external full-text sources linked to from within the ACM bibliographic space are not counted as 'available for download'.
- Average downloads per article = The total number of cumulative downloads divided by the number of articles (including multimedia objects) available for download from ACM's servers.
- Downloads (cumulative) = The cumulative number of times all works by this author have been downloaded from an ACM full-text article server since the downloads were first counted in May 2003. The counts displayed are updated monthly and are therefore 0-31 days behind the current date. Robotic activity is scrubbed from the download statistics.
- Downloads (12 months) = The cumulative number of times all works by this author have been downloaded from an ACM full-text article server over the last 12-month period for which statistics are available. The counts displayed are usually 1-2 weeks behind the current date. (12-month download counts for individual works are displayed with the individual record.)
- Downloads (6 weeks) = The cumulative number of times all works by this author have been downloaded from an ACM full-text article server over the last 6-week period for which statistics are available. The counts displayed are usually 1-2 weeks behind the current date. (6-week download counts for individual works are displayed with the individual record.)
ACM Author-Izer Service
Summary Description
ACM Author-Izer is a unique service that enables ACM authors to generate and post links on both their homepage and institutional repository for visitors to download the definitive version of their articles from the ACM Digital Library at no charge.
Downloads from these sites are captured in official ACM statistics, improving the accuracy of usage and impact measurements. Consistently linking to definitive version of ACM articles should reduce user confusion over article versioning.
ACM Author-Izer also extends ACM’s reputation as an innovative “Green Path” publisher, making ACM one of the first publishers of scholarly works to offer this model to its authors.
To access ACM Author-Izer, authors need to establish a free ACM web account. Should authors change institutions or sites, they can utilize the new ACM service to disable old links and re-authorize new links for free downloads from a different site.
How ACM Author-Izer Works
Authors may post ACM Author-Izer links in their own bibliographies maintained on their website and their own institution’s repository. The links take visitors to your page directly to the definitive version of individual articles inside the ACM Digital Library to download these articles for free.
The Service can be applied to all the articles you have ever published with ACM.
Depending on your previous activities within the ACM DL, you may need to take up to three steps to use ACM Author-Izer.
For authors who do not have a free ACM Web Account:
- Go to the ACM DL http://dl.acm.org/ and click SIGN UP. Once your account is established, proceed to next step.
For authors who have an ACM web account, but have not edited their ACM Author Profile page:
- Sign in to your ACM web account and go to your Author Profile page. Click "Add personal information" and add photograph, homepage address, etc. Click ADD AUTHOR INFORMATION to submit change. Once you receive email notification that your changes were accepted, you may utilize ACM Author-izer.
For authors who have an account and have already edited their Profile Page:
- Sign in to your ACM web account, go to your Author Profile page in the Digital Library, look for the ACM Author-izer link below each ACM published article, and begin the authorization process. If you have published many ACM articles, you may find a batch Authorization process useful. It is labeled: "Export as: ACM Author-Izer Service"
ACM Author-Izer also provides code snippets for authors to display download and citation statistics for each “authorized” article on their personal pages. Downloads from these pages are captured in official ACM statistics, improving the accuracy of usage and impact measurements. Consistently linking to the definitive version of ACM articles should reduce user confusion over article versioning.
Note: You still retain the right to post your author-prepared preprint versions on your home pages and in your institutional repositories with DOI pointers to the definitive version permanently maintained in the ACM Digital Library. But any download of your preprint versions will not be counted in ACM usage statistics. If you use these AUTHOR-IZER links instead, usage by visitors to your page will be recorded in the ACM Digital Library and displayed on your page.
FAQ
- Q. What is ACM Author-Izer?
A. ACM Author-Izer is a unique, link-based, self-archiving service that enables ACM authors to generate and post links on either their home page or institutional repository for visitors to download the definitive version of their articles for free.
- Q. What articles are eligible for ACM Author-Izer?
- A. ACM Author-Izer can be applied to all the articles authors have ever published with ACM. It is also available to authors who will have articles published in ACM publications in the future.
- Q. Are there any restrictions on authors to use this service?
- A. No. An author does not need to subscribe to the ACM Digital Library nor even be a member of ACM.
- Q. What are the requirements to use this service?
- A. To access ACM Author-Izer, authors need to have a free ACM web account, must have an ACM Author Profile page in the Digital Library, and must take ownership of their Author Profile page.
- Q. What is an ACM Author Profile Page?
- A. The Author Profile Page initially collects all the professional information known about authors from the publications record as known by the ACM Digital Library. The Author Profile Page supplies a quick snapshot of an author's contribution to the field and some rudimentary measures of influence upon it. Over time, the contents of the Author Profile page may expand at the direction of the community. Please visit the ACM Author Profile documentation page for more background information on these pages.
- Q. How do I find my Author Profile page and take ownership?
- A. You will need to take the following steps:
- Create a free ACM Web Account
- Sign-In to the ACM Digital Library
- Find your Author Profile Page by searching the ACM Digital Library for your name
- Find the result you authored (where your author name is a clickable link)
- Click on your name to go to the Author Profile Page
- Click the "Add Personal Information" link on the Author Profile Page
- Wait for ACM review and approval; generally less than 24 hours
- Q. Why does my photo not appear?
- A. Make sure that the image you submit is in .jpg or .gif format and that the file name does not contain special characters
- Q. What if I cannot find the Add Personal Information function on my author page?
- A. The ACM account linked to your profile page is different than the one you are logged into. Please logout and login to the account associated with your Author Profile Page.
- Q. What happens if an author changes the location of his bibliography or moves to a new institution?
- A. Should authors change institutions or sites, they can utilize ACM Author-Izer to disable old links and re-authorize new links for free downloads from a new location.
- Q. What happens if an author provides a URL that redirects to the author’s personal bibliography page?
- A. The service will not provide a free download from the ACM Digital Library. Instead the person who uses that link will simply go to the Citation Page for that article in the ACM Digital Library where the article may be accessed under the usual subscription rules.
However, if the author provides the target page URL, any link that redirects to that target page will enable a free download from the Service.
- Q. What happens if the author’s bibliography lives on a page with several aliases?
- A. Only one alias will work, whichever one is registered as the page containing the author’s bibliography. ACM has no technical solution to this problem at this time.
- Q. Why should authors use ACM Author-Izer?
- A. ACM Author-Izer lets visitors to authors’ personal home pages download articles for no charge from the ACM Digital Library. It allows authors to dynamically display real-time download and citation statistics for each “authorized” article on their personal site.
- Q. Does ACM Author-Izer provide benefits for authors?
- A. Downloads of definitive articles via Author-Izer links on the authors’ personal web page are captured in official ACM statistics to more accurately reflect usage and impact measurements.
Authors who do not use ACM Author-Izer links will not have downloads from their local, personal bibliographies counted. They do, however, retain the existing right to post author-prepared preprint versions on their home pages or institutional repositories with DOI pointers to the definitive version permanently maintained in the ACM Digital Library.
- Q. How does ACM Author-Izer benefit the computing community?
- A. ACM Author-Izer expands the visibility and dissemination of the definitive version of ACM articles. It is based on ACM’s strong belief that the computing community should have the widest possible access to the definitive versions of scholarly literature. By linking authors’ personal bibliography with the ACM Digital Library, user confusion over article versioning should be reduced over time.
In making ACM Author-Izer a free service to both authors and visitors to their websites, ACM is emphasizing its continuing commitment to the interests of its authors and to the computing community in ways that are consistent with its existing subscription-based access model.
- Q. Why can’t I find my most recent publication in my ACM Author Profile Page?
- A. There is a time delay between publication and the process which associates that publication with an Author Profile Page. Right now, that process usually takes 4-8 weeks.
- Q. How does ACM Author-Izer expand ACM’s “Green Path” Access Policies?
- A. ACM Author-Izer extends the rights and permissions that authors retain even after copyright transfer to ACM, which has been among the “greenest” publishers. ACM enables its author community to retain a wide range of rights related to copyright and reuse of materials. They include:
- Posting rights that ensure free access to their work outside the ACM Digital Library and print publications
- Rights to reuse any portion of their work in new works that they may create
- Copyright to artistic images in ACM’s graphics-oriented publications that authors may want to exploit in commercial contexts
- All patent rights, which remain with the original owner