SIGIR2021接受论文列表公布！151篇论文都在这了！

2021 年 4 月 28 日 专知

4月15日，中国计算机学会（CCF）推荐的A类国际学术会议SIGIR 2021论文接收结果公布。SIGIR是人工智能领域智能信息检索方向最权威的国际会议。第44届国际计算机学会信息检索大会（The 44rd International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2021）计划于2021年7月11日-7月15日以线上会议形式召开。这次会议共收到720篇长文投稿，仅有151篇长文被录用，录用率约21%。

https://sigir.org/sigir2021/accepted-papers/

A Study of Defensive Methods to Protect Visual Recommendation Against Adversarial Manipulation of Images
Vito Walter Anelli, Yashar Deldjoo, Tommaso Di Noia, Daniele Malitesta and Felice Antonio Merra

On Interpretation and Measurement of Soft Attributes for Recommendation
Krisztian Balog, Filip Radlinski and Alexandros Karatzoglou

Learning a Fine-Grained Review-based Transformer Model for Personalized Product Search
Keping Bi, Qingyao Ai and W. Bruce Croft

Files of a Feather Flock Together? Measuring and Modeling How Users Perceive File Similarity in Cloud Storage
Will Brackenbury, Galen Harrison, Kyle Chard, Aaron Elmore and Blase Ur

Self-Supervised Contrastive Learning for Code Retrieval and Summarization via Semantic-Preserving Transformations
Nghi Bui, Yijun Yu and Lingxiao Jiang

Category-aware Collaborative Sequential Recommendation
Renqin Cai, Hongning Wang, Jibang Wu, Chong Wang and Aidan San

DEKR: Description Enhanced Knowledge Graph for Machine Learning Method Recommendation
Xianshuai Cao, Yuliang Shi, Han Yu, Jihu Wang, Xinjun Wang, Zhongmin Yan and Zhiyong Chen

Sequential Recommendation with Graph Convolutional Networks
Jianxin Chang, Chen Gao, Yu Zheng, Yiqun Hui, Yanan Niu, Yang Song, Depeng Jin and Yong Li

Dual Attention Transfer in Session-based Recommendation with Multi Dimensional Integration
Chen Chen, Jie Guo and Bin Song

Structured Graph Convolutional Networks with Stochastic Masks for Recommender Systems
Huiyuan Chen, Lan Wang, Yusan Lin, Michael Yeh, Fei Wang and Hao Yang

AutoDebias: Learning to Debias for Recommendation
Jiawei Chen, Hande Dong, Yang Qiu, Xiangnan He, Xin Xin, Liang Chen, Guli Lin and Keping Yang

Set2setRank: Collaborative Set to Set Ranking for Implicit Feedback based Recommendation
Lei Chen, Le Wu, Kun Zhang, Richang Hong and Meng Wang

DAIR: A Query-Efficient Decision-based Attack on Image Retrieval Systems
Mingyang Chen, Junda Lu, Jianbin Qin, Yi Wang and Wei Wang

Adversarial-Enhanced Hybrid Graph Network for User Identity Linkage
Xiaolin Chen, Xuemeng Song, Guozhen Peng, Shanshan Feng and Liqiang Nie

Long-Tail Hashing
Yong Chen, Yuqing Hou, Shu Leng, Qing Zhang, Zhouchen Lin and Dell Zhang

Learning Recommender Systems with Implicit Feedback via Soft Target Enhancement
Mingyue Cheng, Fajie Yuan, Qi Liu, Shenyang Ge, Zhi Li, Runlong Yu, Defu Lian, Senchao Yuan and Enhong Chen

Looking at CTR Prediction Again: Is Attention All You Need?
Yuan Cheng and Yanbo Xue

Unsupervised Proxy Selection for Session-based Recommender Systems
Junsu Cho, Seongku Kang, Dongmin Hyun and Hwanjo Yu

Evaluation measures based on preference graphs
Charles Clarke, Chengxi Luo and Mark Smucker

Not All Relevance Scores are Equal: Efficient Uncertainty and Calibration Modeling for Deep Retrieval Models
Daniel Cohen, Bhaskar Mitra, Oleg Lesota, Navid Rekabsaz and Carsten Eickhoff

Allowing for The Grounded Use of Temporal Difference Learning in Large Ranking Models via Sub State Updates
Daniel Cohen

Supporting metacognition during exploratory search with the OrgBox
Anita Crescenzi, Austin Ward, Yuan Li and Robert Capra

An Image is Worth a Thousand Terms? Analysis of Visual E-Commerce Search
Arnon Dagan, Ido Guy and Slava Novgorodov

Unified Conversational Recommendation Policy Learning via Graph-based Reinforcement Learning
Yang Deng, Yaliang Li, Fei Sun, Bolin Ding and Wai Lam

Graph Similarity Computation via Differentiable Optimal Assignment
Khoa Doan, Saurav Manchanda, Suchismit Mahapatra and Chandan K Reddy

Legal Judgment Prediction via Relational Learning
Qian Dong and Shuzi Niu

How Do Biased Search Result Rankings Affect User Attitudes on Debated Topics?
Tim Draws, Nava Tintarev, Ujwal Gadiraju, Alessandro Bozzon and Benjamin Timmermans

PreSizE: Predicting Size in E-Commerce using Transformers
Yotam Eshel, Or Levi, Haggai Roitman and Alex Nus

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