{"id":1987,"date":"2023-06-06T02:52:48","date_gmt":"2023-06-06T02:52:48","guid":{"rendered":"https:\/\/sigir.org\/sigir2023\/?page_id=1987"},"modified":"2023-06-16T08:34:00","modified_gmt":"2023-06-16T08:34:00","slug":"short-papers","status":"publish","type":"page","link":"https:\/\/sigir.org\/sigir2023\/program\/accepted-papers\/short-papers\/","title":{"rendered":"Short papers"},"content":{"rendered":"<div id='full_slider_1'  class='avia-fullwidth-slider main_color avia-shadow   avia-builder-el-0  el_before_av_section  avia-builder-el-first   container_wrap sidebar_right'  >\n<style type=\"text\/css\" data-created_by=\"avia_inline_auto\" id=\"style-css-av-l96qjn6c-1741c8d27e9fc182ecd0f05e501bb522\">\n#top #wrap_all .avia-slideshow .av-slideshow-caption.av-l96qjn6c-1741c8d27e9fc182ecd0f05e501bb522__0 .avia-caption-title{\nfont-size:45px;\n}\n#top .avia-slideshow .av-slideshow-caption.av-l96qjn6c-1741c8d27e9fc182ecd0f05e501bb522__0 .avia-caption-content{\nfont-size:25px;\n}\n#top .avia-slideshow .av-slideshow-caption.av-l96qjn6c-1741c8d27e9fc182ecd0f05e501bb522__0 .avia-caption-content p{\nfont-size:25px;\n}\n\n@media only screen and (max-width: 479px){ \n#top #wrap_all .avia-slideshow .av-slideshow-caption.av-l96qjn6c-1741c8d27e9fc182ecd0f05e501bb522__0 .avia-caption-title{\nfont-size:30px;\n}\n}\n<\/style>\n<div  class='avia-slideshow av-l96qjn6c-1741c8d27e9fc182ecd0f05e501bb522 av-control-default avia-slideshow-no scaling av_slideshow_full avia-slide-slider av-default-height-applied   avia-slideshow-1'  data-size='no scaling'  data-lightbox_size='large'  data-animation='slide'  data-conditional_play=''  data-ids='1803'  data-video_counter='0'  data-autoplay='false'  data-bg_slider='false'  data-slide_height=''  data-handle='av_slideshow_full'  data-interval='5'  data-class=''  data-extra_class=' '  data-el_id=''  data-css_id=''  data-scroll_down=''  data-control_layout='av-control-default'  data-custom_markup=''  data-perma_caption=''  data-autoplay_stopper=''  data-image_attachment=''  data-min_height='200px'  data-lazy_loading='disabled'  data-default-height='38.072916666667'  data-stretch=''  data-src=''  data-position='top left'  data-repeat='no-repeat'  data-attach='scroll'  data-img_scrset=''  data-av-desktop-hide=''  data-av-medium-hide=''  data-av-small-hide=''  data-av-mini-hide=''  data-id=''  data-custom_class=''  data-template_class=''  data-av_uid='av-l96qjn6c'  data-sc_version='1.0'  data-heading_tag=''  data-heading_class=''  data-min_width='526px'   itemprop=\"image\" itemscope=\"itemscope\" itemtype=\"https:\/\/schema.org\/ImageObject\" ><ul class='avia-slideshow-inner ' style='padding-bottom: 38.072916666667%;'><li  class='avia-slideshow-slide av-l96qjn6c-1741c8d27e9fc182ecd0f05e501bb522__0  av-single-slide slide-1 ' ><div data-rel='slideshow-1' class='avia-slide-wrap '   ><div class='av-slideshow-caption av-l96qjn6c-1741c8d27e9fc182ecd0f05e501bb522__0 caption_fullwidth caption_center'><div class=\"container caption_container\"><div class=\"slideshow_caption\"><div class=\"slideshow_inner_caption\"><div class=\"slideshow_align_caption\"><h2 class='avia-caption-title '  itemprop=\"name\" >Short papers<\/h2><\/div><\/div><\/div><\/div><\/div><img decoding=\"async\" class=\"wp-image-1803 avia-img-lazy-loading-not-1803\"  src=\"https:\/\/sigir.org\/sigir2023\/wp-content\/uploads\/2023\/05\/\u967d\u660e\u5c711.jpg\" width=\"1920\" height=\"731\" title='\u967d\u660e\u5c711' alt=''  itemprop=\"thumbnailUrl\"  style='min-height:200px; min-width:526px; ' srcset=\"https:\/\/sigir.org\/sigir2023\/wp-content\/uploads\/2023\/05\/\u967d\u660e\u5c711.jpg 1920w, https:\/\/sigir.org\/sigir2023\/wp-content\/uploads\/2023\/05\/\u967d\u660e\u5c711-300x114.jpg 300w, https:\/\/sigir.org\/sigir2023\/wp-content\/uploads\/2023\/05\/\u967d\u660e\u5c711-1030x392.jpg 1030w, https:\/\/sigir.org\/sigir2023\/wp-content\/uploads\/2023\/05\/\u967d\u660e\u5c711-768x292.jpg 768w, https:\/\/sigir.org\/sigir2023\/wp-content\/uploads\/2023\/05\/\u967d\u660e\u5c711-1536x585.jpg 1536w, https:\/\/sigir.org\/sigir2023\/wp-content\/uploads\/2023\/05\/\u967d\u660e\u5c711-1500x571.jpg 1500w, https:\/\/sigir.org\/sigir2023\/wp-content\/uploads\/2023\/05\/\u967d\u660e\u5c711-705x268.jpg 705w, https:\/\/sigir.org\/sigir2023\/wp-content\/uploads\/2023\/05\/\u967d\u660e\u5c711-845x321.jpg 845w\" sizes=\"(max-width: 1920px) 100vw, 1920px\" \/><div class='av-section-color-overlay' style='opacity: 0.3; background-color: #000000; '><\/div><\/div><\/li><\/ul><\/div><\/div>\n<div id='av_section_1'  class='avia-section av-5lyajc-3134daf831fbcef5bc794ceffe33e473 main_color avia-section-default avia-no-border-styling  avia-builder-el-1  el_after_av_slideshow_full  avia-builder-el-last  avia-bg-style-scroll container_wrap sidebar_right'  ><div class='container av-section-cont-open' ><div class='template-page content  av-content-small alpha units'><div class='post-entry post-entry-type-page post-entry-1987'><div class='entry-content-wrapper clearfix'>\n\n<style type=\"text\/css\" data-created_by=\"avia_inline_auto\" id=\"style-css-av-a0jsj-29b790c6784190c922b8c762aefb1221\">\n.flex_column.av-a0jsj-29b790c6784190c922b8c762aefb1221{\npadding:0 40px 0 40px;\n}\n<\/style>\n<div class='flex_column av-a0jsj-29b790c6784190c922b8c762aefb1221 av_one_full  avia-builder-el-2  avia-builder-el-no-sibling  first flex_column_div '   ><p>\n<style type=\"text\/css\" data-created_by=\"avia_inline_auto\" id=\"style-css-av-lhe77hc3-364024c23e9a46735457c9f58a6dca89\">\n#top .hr.hr-invisible.av-lhe77hc3-364024c23e9a46735457c9f58a6dca89{\nmargin-top:-10px;\nheight:1px;\n}\n<\/style>\n<div  class='hr av-lhe77hc3-364024c23e9a46735457c9f58a6dca89 hr-invisible  avia-builder-el-3  el_before_av_textblock  avia-builder-el-first  av-small-hide av-mini-hide'><span class='hr-inner '><span class=\"hr-inner-style\"><\/span><\/span><\/div><br \/>\n<section  class='av_textblock_section av-p5s2ab-a915d3aefa4550e5cedcf5cee10cd55f'  itemscope=\"itemscope\" itemtype=\"https:\/\/schema.org\/CreativeWork\" ><div class='avia_textblock text_justify'  itemprop=\"text\" ><p><strong>\u25cf A Lightweight Constrained Generation Alternative for Query-focused Summarization<\/strong><br \/>\nZhichao Xu, Daniel Cohen<\/p>\n<p><strong>\u25cf A Mathematical Word Problem Generator with Structure Planning and Knowledge Enhancement<\/strong><br \/>\nLonghu Qin, Jiayu Liu, Zhenya Huang, Kai Zhang, Qi Liu, Binbin Jin, Enhong Chen<\/p>\n<p><strong>\u25cf Mixup-based Unified Framework to Overcome Gender Bias Resurgence<\/strong><br \/>\nLiu Yu, Yuzhou Mao, Jin Wu, Fan Zhou<\/p>\n<p><strong>\u25cf A Model-Agnostic Popularity Debias Training Framework for Click-Through Rate Prediction in Recommender System<\/strong><br \/>\nFan Zhang, Qijie Shen<\/p>\n<p><strong>\u25cf A Simple yet Effective Framework for Few-Shot Aspect-Based Sentiment Analysis<\/strong><br \/>\nZengzhi Wang, Qiming Xie, Rui Xia<\/p>\n<p><strong>\u25cf A Static Pruning Study on Sparse Neural Retrievers<\/strong><br \/>\nCarlos Lassance, Simon Lupart, Herv\u00e9 D\u00e9jean, Stephane Clinchant, Nicola Tonellotto<\/p>\n<p><strong>\u25cf A Unified Formulation for the Frequency Distribution of Word Frequencies using the Inverse Zipf&#8217;s Law<\/strong><br \/>\nCan Ozbey, Talha Colakoglu, M. Safak Bilici, Ekin Can Erkus<\/p>\n<p><strong>\u25cf Adapting Learned Sparse Retrieval for Long Documents<\/strong><br \/>\nThong Nguyen, Sean Macavaney, Andrew Yates<\/p>\n<p><strong>\u25cf ADL: Adaptive Distribution Learning Framework for Multi-Scenario CTR Prediction<\/strong><br \/>\nJinyun Li, Huiwen Zheng, Yuanlin Liu, Minfang Lu, Lixia Wu, Haoyuan Hu<\/p>\n<p><strong>\u25cf Adversarial Meta Prompt Tuning for Open Compound Domain Adaptive Intent Detection<\/strong><br \/>\nFeiteng Fang, Min Yang, Chengming Li, Ruifeng Xu<\/p>\n<p><strong>\u25cf Affective Relevance<\/strong><br \/>\nTuukka Ruotsalo, Kalle M\u00e4kel\u00e4, Michiel M. Spap\u00e9, Luis A. Leiva<\/p>\n<p><strong>\u25cf Popularity Debiasing from Exposure to Interaction in Collaborative Filtering<\/strong><br \/>\nYuanhao Liu, Qi Cao, Huawei Shen, Yunfan Wu, Shuchang Tao, Xueqi Cheng<\/p>\n<p><strong>\u25cf Always Strengthen Your Strengths: A Drift-Aware Incremental Learning Framework for CTR Prediction<\/strong><br \/>\nCongcong Liu, Fei Teng, Xiwei Zhao, Zhangang Lin, Jinghe Hu, Jingping Shao<\/p>\n<p><strong>\u25cf Attacking Pre-trained Recommendation<\/strong><br \/>\nYiqing Wu, Ruobing Xie, Zhao Zhang, Yongchun Zhu, Fuzhen Zhuang, Jie Zhou, Yongjun Xu, Qing He<\/p>\n<p><strong>\u25cf Attention-guided Multi-step Fusion: A Hierarchical Fusion Network for Multimodal Recommendation<\/strong><br \/>\nYan Zhou, Jie Guo, Hao Sun, Bin Song, Fei Richard Yu<\/p>\n<p><strong>\u25cf Attention Mixtures for Time-Aware Sequential Recommendation<\/strong><br \/>\nViet Anh Tran, Guillaume Salha-Galvan, Bruno Sguerra, Romain Hennequin<\/p>\n<p><strong>\u25cf Augmenting Passage Representations with Query Generation for Enhanced Cross-Lingual Dense Retrieval<\/strong><br \/>\nShengyao Zhuang, Linjun Shou, Guido Zuccon<\/p>\n<p><strong>\u25cf AutoDPQ: Automated Differentiable Product Quantization for Embedding Compression<\/strong><br \/>\nXin Gan, Yuhao Wang, Xiangyu Zhao, Wanyu Wang, Yiqi Wang, Zitao Liu<\/p>\n<p><strong>\u25cf Bayesian Knowledge-driven Critiquing with Indirect Evidence<\/strong><br \/>\nArmin Toroghi, Griffin Floto, Zhenwei Tang, Scott Sanner<\/p>\n<p><strong>\u25cf Behavior Modeling for Point of Interest Search<\/strong><br \/>\nHaitian Chen, Qingyao Ai, Zhijing Wu, Zhihong Wang, Yiqun Liu, Min Zhang, Shaoping Ma, Juan Hu, Naiqiang Tan, Hua Chai<\/p>\n<p><strong>\u25cf Benchmarking Middle-Trained Language Models for Neural Search<\/strong><br \/>\nHerv\u00e9 D\u00e9jean, Stephane Clinchant, Carlos Lassance, Simon Lupart, Thibault Formal<\/p>\n<p><strong>\u25cf BioAug: Conditional Generation based Data Augmentation for Low-Resource Biomedical NER<\/strong><br \/>\nSreyan Ghosh, Utkarsh Tyagi, Sonal Kumar, Dinesh Manocha<\/p>\n<p><strong>\u25cf BKD:A Bridge-based Knowledge Distillation Method for Click-Through Rate Prediction<\/strong><br \/>\nYin Deng, Yingxin Chen, Xin Dong, Lingchao Pan, Hai Li, Lei Cheng, Linjian Mo<\/p>\n<p><strong>\u25cf Calibration Learning for Few-shot Novel Product Description<\/strong><br \/>\nZheng Liu, Mingjing Wu, Bo Peng, Yichao Liu, Qi Peng, Chong Zou<\/p>\n<p><strong>\u25cf Can Generative LLMs Create Query Variants for Test Collections?<\/strong><br \/>\nMarwah Alaofi, Luke Gallagher, Mark Sanderson, Falk Scholer, Paul Thomas<\/p>\n<p><strong>\u25cf Causal Disentangled Variational Auto-Encoder for Preference Understanding in Recommendation<\/strong><br \/>\nSiyu Wang, Xiaocong Chen, Quan Z. Sheng, Yihong Zhang, Lina Yao<\/p>\n<p><strong>\u25cf CEC: Towards Learning Global Optimized Recommendation through Causality Enhanced Conversion Model<\/strong><br \/>\nRan Le, Guoqing Jiang, Xiufeng Shu, Ruidong Han, Qianzhong Li, Yacheng Li, Xiang Li, Wei Lin<\/p>\n<p><strong>\u25cf Click-Conversion Multi-Task Model with Position Bias Mitigation for Sponsored Search in eCommerce<\/strong><br \/>\nYibo Wang, Yanbing Xue, Bo Liu, Musen Wen, Wenting Zhao, Stephen Guo, Philip S. Yu<\/p>\n<p><strong>\u25cf Computational Versus Perceived Popularity Miscalibration in Recommender Systems<\/strong><br \/>\nOleg Lesota, Gustavo Escobedo, Yashar Deldjoo, Bruce Ferwerda, Simone Kopeinik, Elisabeth Lex, Navid Rekabsaz, Markus Schedl<\/p>\n<p><strong>\u25cf Connecting Unseen Domains: Cross-Domain Invariant Learning in Recommendation<\/strong><br \/>\nYang Zhang, Yue Shen, Dong Wang, Jinjie Gu, Guannan Zhang<\/p>\n<p><strong>\u25cf ConQueR: Contextualized Query Reduction using Search Logs<\/strong><br \/>\nHye-young Kim, Minjin Choi, Sunkyung Lee, Eunseong Choi, Young-In Song, Jongwuk Lee<\/p>\n<p><strong>\u25cf Context-Aware Modeling via Simulated Exposure Page for CTR Prediction<\/strong><br \/>\nXiang Li, Shuwei Chen, Jian Dong, Jin Zhang, Yongkang Wang, Xingxing Wang, Dong Wang<\/p>\n<p><strong>\u25cf Contrastive Learning for Conversion Rate Prediction<\/strong><br \/>\nWentao Ouyang, Rui Dong, Xiuwu Zhang, Chaofeng Guo, Jinmei Luo, Xiangzheng Liu, Yanlong Du<\/p>\n<p><strong>\u25cf Curriculum Modeling the Dependence among Targets with Multi-task Learning for Financial Marketing<\/strong><br \/>\nYunpeng Weng, Xing Tang, Liang Chen, Xiuqiang He<\/p>\n<p><strong>\u25cf Decomposing Logits Distillation for Incremental Named Entity Recognition<\/strong><br \/>\nDuzhen Zhang, Yahan Yu, Feilong Chen, Xiuyi Chen<\/p>\n<p><strong>\u25cf Denoise to protect: a method to robustify visual recommenders from adversaries.<\/strong><br \/>\nFelice Antonio Merra, Vito Walter Anelli, Tommaso Di Noia, Daniele Malitesta, Alberto Carlo Maria Mancino<\/p>\n<p><strong>\u25cf DeviceGPT: A Generative Pre-Training Transformer on the Heterogenous Graph for Internet of Things<\/strong><br \/>\nYimo Ren, Jinfa Wang, Hong Li, Hongsong Zhu, Limin Sun<\/p>\n<p><strong>\u25cf Dimension-Prompts Boost Commonsense Consolidation<\/strong><br \/>\nJiazhan Feng, Chongyang Tao, Tao Shen, Chang Liu, Dongyan Zhao<\/p>\n<p><strong>\u25cf Disentangling User Conversations with Voice Assistants for Online Shopping<\/strong><br \/>\nNikhita Vedula, Marcus Collins, Oleg Rokhlenko<\/p>\n<p><strong>\u25cf DocGraphLM: Documental graph language model for information extraction<\/strong><br \/>\nDongsheng Wang, Zhiqiang Ma, Armineh Nourbakhsh, Kang Gu, Sameena Shah<\/p>\n<p><strong>\u25cf Edge-cloud Collaborative Learning with Federated and Centralized Features<\/strong><br \/>\nZexi Li, Qunwei Li, Yi Zhou, Wenliang Zhong, Guannan Zhang, Chao Wu<\/p>\n<p><strong>\u25cf SLIM: Sparsified Late Interaction for Multi-Vector Retrieval with Inverted Indexes<\/strong><br \/>\nMinghan Li, Sheng-Chieh Lin, Xueguang Ma, Jimmy Lin<\/p>\n<p><strong>\u25cf Evaluating Cross-modal Generative Models Using Retrieval Task<\/strong><br \/>\nShivangi Bithel, Srikanta Bedathur<\/p>\n<p><strong>\u25cf Event-Aware Adaptive Clustering Uplift Network for Insurance Creative Ranking<\/strong><br \/>\nWanjie Tao, Huihui Liu, Xuqi Li, Qun Dai, Hong Wen, Zulong Chen<\/p>\n<p><strong>\u25cf Examining the Impact of Uncontrolled Variables on Physiological Signals in User Studies for Information Processing Activities<\/strong><br \/>\nKaixin Ji, Damiano Spina, Danula Hettiachchi, Flora Dilys Salim, Falk Scholer<\/p>\n<p><strong>\u25cf Explain Like I am BM25: Interpreting a Dense Model&#8217;s Ranked-List with a Sparse Approximation<\/strong><br \/>\nMichael Llordes, Debasis Ganguly, Sumit Bhatia, Chirag Agarwal<\/p>\n<p><strong>\u25cf Exploiting Cluster-Skipping Inverted Index for Semantic Place Retrieval<\/strong><br \/>\nEnes Recep Cinar, Ismail Sengor Altingovde<\/p>\n<p><strong>\u25cf Exploiting Ubiquitous Mentions for Document-Level Relation Extraction<\/strong><br \/>\nRuoyu Zhang, Yanzeng Li, Minhao Zhang, Lei Zou<\/p>\n<p><strong>\u25cf Exploration of Unranked Items in Safe Online Learning to Re-Rank<\/strong><br \/>\nHiroaki Shiino, Kaito Ariu, Kenshi Abe, Riku Togashi<\/p>\n<p><strong>\u25cf Fairness for both Readers and Authors: Evaluating Summaries of User Generated Content<\/strong><br \/>\nGarima Chhikara, Kripabandhu Ghosh, Saptarshi Ghosh, Abhijnan Chakraborty<\/p>\n<p><strong>\u25cf Faster Dynamic Pruning via Reordering of Documents in Inverted Indexes<\/strong><br \/>\nErman Yafay, Ismail Sengor Altingovde<\/p>\n<p><strong>\u25cf FINAL:Factorized Interaction Layer for CTR Prediction<\/strong><br \/>\nJieming Zhu, Qinglin Jia, Guohao Cai, Quanyu Dai, Jingjie Li, Zhenhua Dong, Ruiming Tang, Rui Zhang<\/p>\n<p><strong>\u25cf Forget Me Now: Fast and Exact Unlearning in Neighborhood-based Recommendation<\/strong><br \/>\nSebastian Schelter, Mozhdeh Ariannezhad, Maarten De Rijke<\/p>\n<p><strong>\u25cf Friend Ranking in Online Games via Pre-training Edge Transformers<\/strong><br \/>\nLiang Yao, Jiazhen Peng, Shenggong Ji, Qiang Liu, Hongyun Cai, Feng He, Xu Cheng<\/p>\n<p><strong>\u25cf Gated Attention with Asymmetric Regularization for Transformer-based Continual Graph Learning<\/strong><br \/>\nHongxiang Lin, Ruiqi Jia, Xiaoqing Lyu<\/p>\n<p><strong>\u25cf Generative Relevance Feedback with Large Language Models<\/strong><br \/>\nIain Mackie, Shubham Chatterjee, Jeffrey Dalton<\/p>\n<p><strong>\u25cf Gradient Coordination for Quantifying and Maximizing Knowledge Transference in Multi-Task Learning<\/strong><br \/>\nXuanhua Yang, Jianxin Zhao, Shaoguo Liu, Liang Wang, Bo Zheng<\/p>\n<p><strong>\u25cf Graph Collaborative Signals Denoising and Augmentation for Recommendation<\/strong><br \/>\nZiwei Fan, Ke Xu, Zhang Dong, Hao Peng, Jiawei Zhang, Philip S. Yu<\/p>\n<p><strong>\u25cf Neighborhood-based Hard Negative Mining for Sequential Recommendation<\/strong><br \/>\nLu Fan, Jiashu Pu, Rongsheng Zhang, Xiao-Ming Wu<\/p>\n<p><strong>\u25cf Hierarchical Type Enhanced Negative Sampling for Knowledge Graph Embedding<\/strong><br \/>\nZhenzhou Lin, Zishuo Zhao, Jingyou Xie, Ying Shen<\/p>\n<p><strong>\u25cf HiPrompt: Few-Shot Biomedical Knowledge Fusion via Hierarchy-Oriented Prompting<\/strong><br \/>\nJiaying Lu, Jiaming Shen, Bo Xiong, Wenjing Ma, Steffen Staab, Carl Yang<\/p>\n<p><strong>\u25cf How Significant Attributes are in the Community Detection of Attributed Multiplex Networks<\/strong><br \/>\nJunwei Cheng, Chaobo He, Kunlin Han, Wenjie Ma, Yong Tang<\/p>\n<p><strong>\u25cf HyperFormer: Learning Expressive Sparse Feature Representations via Hypergraphs<\/strong><br \/>\nKaize Ding, Albert Jiongqian Liang, Bryan Perozzi, Ting Chen, Ruoxi Wang, Lichan Hong, Ed H. Chi, Huan Liu, Derek Zhiyuan Cheng<\/p>\n<p><strong>\u25cf Best Prompts for Text-to-Image Models and How to Find Them<\/strong><br \/>\nNikita Pavlichenko, Dmitry Ustalov<\/p>\n<p><strong>\u25cf Improved Vector Quantization For Dense Retrieval with Contrastive Distillation<\/strong><br \/>\nJames O&#8217; Neill, Sourav Dutta<\/p>\n<p><strong>\u25cf Improving Conversational Passage Re-ranking with View Ensemble<\/strong><br \/>\nJia-Huei Ju, Sheng-Chieh Lin, Ming-Feng Tsai, Chuan-Ju Wang<\/p>\n<p><strong>\u25cf Improving News Recommendation via Bottlenecked Multi-task Pre-training<\/strong><br \/>\nXiongfeng Xiao, Qing Li, Songlin Liu, Kun Zhou<\/p>\n<p><strong>\u25cf Inference at Scale<\/strong><br \/>\nNgozi Ihemelandu, Michael D. Ekstrand<\/p>\n<p><strong>\u25cf LADER: Log-Augmented DEnse Retrieval for Biomedical Literature Search<\/strong><br \/>\nQiao Jin, Andrew Shin, Zhiyong Lu<\/p>\n<p><strong>\u25cf LAPCA: Language-Agnostic Pretraining with Cross-Lingual Alignment<\/strong><br \/>\nDmitry Abulkhanov, Nikita Sorokin, Sergey Nikolenko, Valentin Malykh<\/p>\n<p><strong>\u25cf Learning from Crowds with Annotation Reliability<\/strong><br \/>\nZhi Cao, Enhong Chen, Ye Huang, Shuanghong Shen, Zhenya Huang<\/p>\n<p><strong>\u25cf Learning Through Interpolative Augmentation of Dynamic Curvature Spaces<\/strong><br \/>\nParth Chhabra, Atula Tejaswi Neerkaje, Shivam Agarwal, Ramit Sawhney, Megh Thakkar, Preslav Nakov, Sudheer Chava<\/p>\n<p><strong>\u25cf Learning to Ask Clarification Questions with Spatial Reasoning<\/strong><br \/>\nYang Deng, Shuaiyi Li, Wai Lam<\/p>\n<p><strong>\u25cf Learning to Ask Questions for Zero-shot Dialogue State Tracking<\/strong><br \/>\nDiogo Tavares, Joao Magalhaes, David Semedo, Alexander Rudnicky<\/p>\n<p><strong>\u25cf Limitations of Open-Domain Question Answering Benchmarks for Document-level Reasoning<\/strong><br \/>\nEhsan Kamalloo, Charles L. A. Clarke, Davood Rafiei<\/p>\n<p><strong>\u25cf LogicRec: Recommendation with Users&#8217; Logical Requirements<\/strong><br \/>\nZhenwei Tang, Griffin Floto, Armin Toroghi, Shichao Pei, Xiangliang Zhang, Scott Sanner<\/p>\n<p><strong>\u25cf Look Ahead: Improving the Accuracy of Time-Series Forecasting by Previewing Future Time Features<\/strong><br \/>\nSeonmin Kim, Dong-Kyu Chae<\/p>\n<p><strong>\u25cf LOVF: Layered Organic View Fusion for Click-through Rate Prediction in Online Advertising<\/strong><br \/>\nLingwei Kong, Lu Wang, Xiwei Zhao, Junsheng Jin, Zhangang Lin, Jinghe Hu, Jingping Shao<\/p>\n<p><strong>\u25cf MA-MRC: A Multi-answer Machine Reading Comprehension Dataset<\/strong><br \/>\nZhiang Yue, Jingping Liu, Cong Zhang, Chao Wang, Haiyun Jiang, Yue Zhang, Xianyang Tian, Zhedong Cen, Yanghua Xiao, Tong Ruan<\/p>\n<p><strong>\u25cf Matching Point of Interests and Travel Blog with Multi-view Information Fusion<\/strong><br \/>\nShuokai Li, Jingbo Zhou, Jizhou Huang, Hao Chen, Fuzhen Zhuang, Qing He, Dejing Dou<\/p>\n<p><strong>\u25cf MaxSimE: Explaining Transformer-based Semantic Similarity via Contextualized Best Matching Token Pairs<\/strong><br \/>\nEduardo Brito, Henri Iser<\/p>\n<p><strong>\u25cf MDDL: A Framework for Reinforcement Learning-based Position Allocation in Multi-Channel Fee<\/strong><br \/>\nXiaowen Shi, Ze Wang, Yuanying Cai, Xiaoxu Wu, Fan Yang, Guogang Liao, Yongkang Wang, Xingxing Wang, Dong Wang<\/p>\n<p><strong>\u25cf MDKG: Graph-Based Medical Knowledge-Guided Dialogue Generation<\/strong><br \/>\nUsman Naseem, Surendrabikram Thapa, Qi Zhang, Liang Hu, Mehwish Nasim<\/p>\n<p><strong>\u25cf Measuring Service-Level Learning Effects in Search Via Query-Randomized Experiments<\/strong><br \/>\nPaul Musgrave, Cuize Han, Parth Gupta<\/p>\n<p><strong>\u25cf Mining Interest Trends and Adaptively Assigning Sample Weight for Session-based Recommendation<\/strong><br \/>\nKai Ouyang, Xianghong Xu, Miaoxin Chen, Zuotong Xie, Hai-Tao Zheng, Shuangyong Song, Yu Zhao<\/p>\n<p><strong>\u25cf Model-free Reinforcement Learning with Stochastic Reward Stabilization for Recommender Systems<\/strong><br \/>\nTianchi Cai, Shenliao Bao, Jiyan Jiang, Shiji Zhou, Wenpeng Zhang, Lihong Gu, Jinjie Gu, Guannan Zhang<\/p>\n<p><strong>\u25cf Modeling Orders of User Behaviors via Differentiable Sorting: A Multi-task Framework to Predicting User Post-click Conversion<\/strong><br \/>\nMenghan Wang, Jinming Yang, Yuchen Guo, Yuming Shen, Mengying Zhu, Yanlin Wang<\/p>\n<p><strong>\u25cf Multi-Grained Topological Pre-Training of Language Models in Sponsored Search<\/strong><br \/>\nZhoujin Tian, Chaozhuo Li, Zhiqiang Zuo, Zengxuan Wen, Xinyue Hu, Xiao Han, Haizhen Huang, Senzhang Wang, Weiwei Deng, Xing Xie, Qi Zhang<\/p>\n<p><strong>\u25cf Multi-grained Representation Learning for Cross-modal Retrieval<\/strong><br \/>\nShengwei Zhao, Linhai Xu, Yuying Liu, Shaoyi Du<\/p>\n<p><strong>\u25cf Multiple topics community detection in attributed networks<\/strong><br \/>\nChaobo He, Junwei Cheng, Guohua Chen, Yong Tang<\/p>\n<p><strong>\u25cf NC^2T: Novel Curriculum Learning Approaches for Cross-Prompt Trait Scoring<\/strong><br \/>\nYejin Lee, Seokwon Jeong, Hongjin Kim, Tae-Il Kim, Sung-Won Choi, Harksoo Kim<\/p>\n<p><strong>\u25cf Offline Pseudo Relevance Feedback for Efficient and Effective Single-pass Dense Retrieval<\/strong><br \/>\nXueru Wen, Xiaoyang Chen, Xuanang Chen, Ben He, Le Sun<\/p>\n<p><strong>\u25cf On Answer Position Bias in Transformers for Question Answering<\/strong><br \/>\nRafael Glater, Rodrygo L. T. Santos<\/p>\n<p><strong>\u25cf On the Effects of Regional Spelling Conventions in Retrieval Models<\/strong><br \/>\nAndreas Chari, Sean MacAvaney, Iadh Ounis<\/p>\n<p><strong>\u25cf On the Impact of Data Quality on Image Classification Fairness<\/strong><br \/>\nAki Barry, Lei Han, Gianluca Demartini<\/p>\n<p><strong>\u25cf One-Shot Labeling for Automatic Relevance Estimation<\/strong><br \/>\nSean Macavaney, Luca Soldaini<\/p>\n<p><strong>\u25cf Optimizing Reciprocal Rank with Bayesian Average for improved Next Item Recommendation<\/strong><br \/>\nXiangkui Lu, Jun Wu, Jianbo Yuan<\/p>\n<p><strong>\u25cf Patterns of gender-specializing query reformulation<\/strong><br \/>\nAmifa Raj, Bhaskar Mitra, Nick Craswell, Michael Ekstrand<\/p>\n<p><strong>\u25cf Personalized Dynamic Recommender System for Investors<\/strong><br \/>\nTakehiro Takayanagi, Chung-Chi Chen, Kiyoshi Izumi<\/p>\n<p><strong>\u25cf Personalized Showcases: Generating Multi-Modal Explanations for Recommendations<\/strong><br \/>\nAn Yan, Zhankui He, Jiacheng Li, Tianyang Zhang, Julian Mcauley<\/p>\n<p><strong>\u25cf PersonalTM: Transformer Memory for Personalized Retrieval<\/strong><br \/>\nRuixue Lian, Sixing Lu, Clint Solomon, Gustavo Aguilar, Pragaash Ponnusamy, Jialong Han, Chengyuan Ma, Chenlei Guo<\/p>\n<p><strong>\u25cf PiTL: Cross-modal Retrieval with Weakly-supervised Vision-language Pre-training via Prompting<\/strong><br \/>\nZixin Guo, Tzu-Jui Julius Wang, Selen Pehlivan, Abduljalil Radman, Jorma Laaksonen<\/p>\n<p><strong>\u25cf Power Norm Based Lifelong Learning for Paraphrase Generations<\/strong><br \/>\nDingcheng Li, Peng Yang, Yue Zhang, Ping Li<\/p>\n<p><strong>\u25cf Prediction then Correction: An Abductive Prediction Correction Method for Sequential Recommendation<\/strong><br \/>\nYulong Huang, Yang Zhang, Qifan Wang, Chenxu Wang, Fuli Feng<\/p>\n<p><strong>\u25cf Priming and Actions: An Analysis in Conversational Search Systems<\/strong><br \/>\nXiao Fu, Aldo Lipani<\/p>\n<p><strong>\u25cf Private Meeting Summarization Without Performance Loss<\/strong><br \/>\nSeolhwa Lee, Anders S\u00f8gaard<\/p>\n<p><strong>\u25cf Prompt Learning to Mitigate Catastrophic Forgetting in Cross-lingual Transfer for Open-domain Dialogue Generation<\/strong><br \/>\nLei Liu, Jimmy Xiangji Huang<\/p>\n<p><strong>\u25cf Quantifying and Leveraging User Fatigue for Interventions in Recommender Systems<\/strong><br \/>\nHitesh Sagtani, Madan Gopal Jhawar, Akshat Gupta, Rishabh Mehrotra<\/p>\n<p><strong>\u25cf Quantifying Ranker Coverage of Different Query Subspaces<\/strong><br \/>\nNegar Arabzadeh, Amin Bigdeli, Radin Hamidi Rad, Ebrahim Bagheri<\/p>\n<p><strong>\u25cf Query-specific Variable Depth Pooling via Query Performance Prediction<\/strong><br \/>\nDebasis Ganguly, Emine Yilmaz<\/p>\n<p><strong>\u25cf RankT5: Fine-Tuning T5 for Text Ranking with Ranking Losses<\/strong><br \/>\nHonglei Zhuang, Zhen Qin, Rolf Jagerman, Kai Hui, Ji Ma, Jing Lu, Jianmo Ni, Xuanhui Wang, Michael Bendersky<\/p>\n<p><strong>\u25cf Rating Prediction in Conversational Task Assistants with Behavioral and Conversational-Flow Features<\/strong><br \/>\nRafael Ferreira, David Semedo, Jo\u00e3o Magalh\u00e3es<\/p>\n<p><strong>\u25cf Read it Twice: Towards Faithfully Interpretable Fact Verification by Revisiting Evidence<\/strong><br \/>\nXuming Hu, Zhaochen Hong, Zhijiang Guo, Lijie Wen, Philip Yu<\/p>\n<p><strong>\u25cf Reducing Spurious Correlations for Relation Extraction by Feature Decomposition and Semantic Augmentation<\/strong><br \/>\nTianshu Yu, Min Yang, Chengming Li, Ruifeng Xu<\/p>\n<p><strong>\u25cf Representation Sparsification with Hybrid Thresholding for Fast SPLADE-based Document Retrieval<\/strong><br \/>\nYifan Qiao, Yingrui Yang, Shanxiu He, Tao Yang<\/p>\n<p><strong>\u25cf Retrieval-Enhanced Generative Model for Large-Scale Knowledge Graph Completion<\/strong><br \/>\nDonghan Yu, Yiming Yang<\/p>\n<p><strong>\u25cf Review-based Multi-intention Contrastive Learning for Recommendation<\/strong><br \/>\nWei Yang, Tengfei Huo, Zhiqiang Liu, Chi Lu<\/p>\n<p><strong>\u25cf RewardTLG: Learning to Temporally Language Grounding from Flexible Reward<\/strong><br \/>\nYawen Zeng, Keyu Pan, Ning Han<\/p>\n<p><strong>\u25cf Robust Causal Inference for Recommender System to Overcome Noisy Confounders<\/strong><br \/>\nZhiheng Zhang, Quanyu Dai, Xu Chen, Zhenhua Dong, Ruiming Tang<\/p>\n<p><strong>\u25cf Rows or Columns? Minimizing Presentation Bias When Comparing Multiple Recommender Systems<\/strong><br \/>\nPatrik Dokoupil, Ladislav Peska, Ludovico Boratto<\/p>\n<p><strong>\u25cf Searching for Products in Virtual Reality: Understanding the Impact of Context and Result Presentation on User Experience<\/strong><br \/>\nAustin Ward, Sandeep Avula, Hao-Fei Cheng, Sheikh Sarwar, Vanessa Murdock, Eugene Agichtein<\/p>\n<p><strong>\u25cf SelfLRE: Self-refining Representation Learning for Low-resource Relation Extraction<\/strong><br \/>\nXuming Hu, Junzhe Chen, Shiao Meng, Lijie Wen, Philip Yu<\/p>\n<p><strong>\u25cf Sharpness-Aware Graph Collaborative Filtering<\/strong><br \/>\nHuiyuan Chen, Chin-Chia Michael Yeh, Yujie Fan, Yan Zheng, Junpeng Wang, Vivian Lai, Mahashweta Das, Hao Yang<\/p>\n<p><strong>\u25cf Simple Approach for Aspect Sentiment Triplet Extraction Using Span-Based Segment Tagging and Dual Extractors<\/strong><br \/>\nDongxu Li, Zhihao Yang, Yuquan Lan, Yunqi Zhang, Hui Zhao, Gang Zhao<\/p>\n<p><strong>\u25cf Simpler is Much Faster: Fair and Independent Inner Product Search<\/strong><br \/>\nKazuyoshi Aoyama, Daichi Amagata, Sumio Fujita, Takahiro Hara<\/p>\n<p><strong>\u25cf Simplifying Content-Based Neural News Recommendation: On User Modeling and Training Objectives<\/strong><br \/>\nAndreea Iana, Goran Glava\u0161, Heiko Paulheim<\/p>\n<p><strong>\u25cf SimTDE: Simple Transformer Distillation for Sentence Embeddings<\/strong><br \/>\nJian Xie, Xin He, Jiyang Wang, Zimeng Qiu, Ali Kebarighotbi, Farhad Ghassemi<\/p>\n<p><strong>\u25cf Sinkhorn Transformations for Single-Query Postprocessing in Text-Video Retrieval<\/strong><br \/>\nKonstantin Yakovlev, Gregory Polyakov, Ilseyar Alimova, Alexander Podolskiy, Andrey Bout, Sergey Nikolenko, Irina Piontkovskaya<\/p>\n<p><strong>\u25cf SparseEmbed: Learning Sparse Lexical Representations with Contextual Embeddings for Retrieval<\/strong><br \/>\nWeize Kong, Jeffrey M. Dudek, Cheng Li, Mingyang Zhang, Michael Bendersky<\/p>\n<p><strong>\u25cf Surprise: Result List Truncation via Extreme Value Theory<\/strong><br \/>\nDara Bahri, Che Zheng, Yi Tay, Donald Metzler, Andrew Tomkins<\/p>\n<p><strong>\u25cf ExaRanker: Synthetic Explanations Improve Neural Rankers<\/strong><br \/>\nFernando Ferraretto, Thiago Laitz, Roberto Lotufo, Rodrigo Nogueira<\/p>\n<p><strong>\u25cf TAML: Time-Aware Meta Learning for Cold-Start Problem in News Recommendation<\/strong><br \/>\nJingyuan Li, Yue Zhang, Xuan Lin, Xinxing Yang, Ge Zhou, Longfei Li, Hong Chen, Jun Zhou<\/p>\n<p><strong>\u25cf Text-to-Motion Retrieval: Towards Joint Understanding of Human Motion Data and Natural Language<\/strong><br \/>\nNicola Messina, Jan Sedmidubsky, Fabrizio Falchi, Tom\u00e1\u0161 Rebok<\/p>\n<p><strong>\u25cf The Dark Side of Explanations: Poisoning Recommender Systems with Counterfactual Examples<\/strong><br \/>\nZiheng Chen, Fabrizio Silvestri, Jia Wang, Yongfeng Zhang, Gabriele Tolomei<\/p>\n<p><strong>\u25cf The tale of two MSMARCO &#8211; and their reproducibility issues<\/strong><br \/>\nCarlos Lassance, Stephane Clinchant<\/p>\n<p><strong>\u25cf Think Rationally about What You See: Continuous Rationale Extraction for Relation Extraction<\/strong><br \/>\nXuming Hu, Zhaochen Hong, Chenwei Zhang, Irwin King, Philip Yu<\/p>\n<p><strong>\u25cf Towards Robust Knowledge Tracing Models via k-Sparse Attention<\/strong><br \/>\nShuyan Huang, Zitao Liu, Xiangyu Zhao, Weiqi Luo, Jian Weng<\/p>\n<p><strong>\u25cf TripSafe: Retrieving Safety-related Abnormal Trips in Real-time with Trajectory Data<\/strong><br \/>\nYueyang Su, Di Yao, Xiaolei Zhou, Yuxuan Zhang, Yunxia Fan, Lu Bai, Jingping Bi<\/p>\n<p><strong>\u25cf TrustSGCN: Learning Trustworthiness on Edge Signs for Effective Signed Graph Convolutional Networks<\/strong><br \/>\nMin-Jeong Kim, Yeon-Chang Lee, Sang-Wook Kim<\/p>\n<p><strong>\u25cf uCTRL: Unbiased Contrastive Representation Learning via Alignment and Uniformity for Collaborative Filtering<\/strong><br \/>\nJae-Woong Lee, Seongmin Park, Mincheol Yoon, Jongwuk Lee<\/p>\n<p><strong>\u25cf Unbiased Pairwise Learning from Implicit Feedback for Recommender Systems without Biased Variance Control<\/strong><br \/>\nYi Ren, Hongyan Tang, Jiangpeng Rong, Siwen Zhu<\/p>\n<p><strong>\u25cf Uncertainty-aware Consistency Learning for Cold-Start Item Recommendation<\/strong><br \/>\nTaichi Liu, Chen Gao, Zhenyu Wang, Dong Li, Jianye Hao, Depeng Jin, Yong Li<\/p>\n<p><strong>\u25cf Uncertainty-based Heterogeneous Privileged Knowledge Distillation for Recommendation System<\/strong><br \/>\nAng Li, Jian Hu, Ke Ding, Xiaolu Zhang, Jun Zhou, Yong He, Xu Min<\/p>\n<p><strong>\u25cf Unsupervised Dense Retrieval Training with Web Anchors<\/strong><br \/>\nYiqing Xie, Xiao Liu, Chenyan Xiong<\/p>\n<p><strong>\u25cf Unsupervised Dialogue Topic Segmentation with Topic-aware Contrastive Learning<\/strong><br \/>\nHaoyu Gao, Rui Wang, Ting-En Lin, Yuchuan Wu, Min Yang, Fei Huang, Yongbin Li<\/p>\n<p><strong>\u25cf Unsupervised Query Performance Prediction for Neural Models with Pairwise Rank Preferences<\/strong><br \/>\nAshutosh Singh, Debasis Ganguly, Suchana Datta, Craig Mcdonald<\/p>\n<p><strong>\u25cf User-Dependent Learning to Debias for Recommendation<\/strong><br \/>\nFangyuan Luo, Jun Wu<\/p>\n<p><strong>\u25cf Using Entropy for Group Sampling in Pairwise Ranking from implicit feedback<\/strong><br \/>\nYujie Chen, Runlong Yu, Qi Liu, Enhong Chen, Zhenya Huang<\/p>\n<p><strong>\u25cf Weakly-Supervised Scientific Document Classification via Retrieval-Augmented Multi-Stage Training<\/strong><br \/>\nRan Xu, Yue Yu, Joyce Ho, Carl Yang<\/p>\n<p><strong>\u25cf When the Music Stops: Tip-of-the-Tongue Retrieval for Music<\/strong><br \/>\nSamarth Bhargav, Anne Schuth, Claudia Hauff<\/p>\n<p><strong>\u25cf Where Does Your News Come From? Predicting Information Pathways in Social Media<\/strong><br \/>\nAlexander Taylor, Nuan Wen, Po-Nien Kung, Jiaao Chen, Violet Peng, Wei Wang<\/p>\n<p><strong>\u25cf Which Matters Most in Making Fund Investment Decisions? A Multi-granularity Graph Disentangled Learning Framework<\/strong><br \/>\nChunjing Gan, Binbin Hu, Bo Huang, Tianyu Zhao, Yingru Lin, Wenliang Zhong, Zhiqiang Zhang, Jun Zhou, Chuan Shi<\/p>\n<p><strong>\u25cf WSFE: Wasserstein Sub-graph Feature Encoder for Effective User Segmentation in Collaborative Filtering<\/strong><br \/>\nYankai Chen, Yifei Zhang, Menglin Yang, Zixing Song, Chen Ma, Irwin King<\/p>\n<p><strong>\u25cf EmoUS: Simulating User Emotions in Task-Oriented Dialogues<\/strong><br \/>\nHsien-Chin Lin, Shutong Feng, Christian Geishauser, Nurul Lubis, Carel van Niekerk, Michael Heck, Benjamin Ruppik, Renato Vukovic, Milica Ga\u0161i\u0107<\/p>\n<\/div><\/section><\/p><\/div><\/div><\/div><\/div><!-- close content main div --><\/div><\/div><div id='after_section_1'  class='main_color av_default_container_wrap container_wrap sidebar_right'  ><div class='container av-section-cont-open' ><div class='template-page content  av-content-small alpha units'><div class='post-entry post-entry-type-page post-entry-1987'><div class='entry-content-wrapper clearfix'>\n","protected":false},"excerpt":{"rendered":"","protected":false},"author":4,"featured_media":0,"parent":1979,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"footnotes":""},"class_list":["post-1987","page","type-page","status-publish","hentry"],"_links":{"self":[{"href":"https:\/\/sigir.org\/sigir2023\/wp-json\/wp\/v2\/pages\/1987","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/sigir.org\/sigir2023\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/sigir.org\/sigir2023\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/sigir.org\/sigir2023\/wp-json\/wp\/v2\/users\/4"}],"replies":[{"embeddable":true,"href":"https:\/\/sigir.org\/sigir2023\/wp-json\/wp\/v2\/comments?post=1987"}],"version-history":[{"count":7,"href":"https:\/\/sigir.org\/sigir2023\/wp-json\/wp\/v2\/pages\/1987\/revisions"}],"predecessor-version":[{"id":2079,"href":"https:\/\/sigir.org\/sigir2023\/wp-json\/wp\/v2\/pages\/1987\/revisions\/2079"}],"up":[{"embeddable":true,"href":"https:\/\/sigir.org\/sigir2023\/wp-json\/wp\/v2\/pages\/1979"}],"wp:attachment":[{"href":"https:\/\/sigir.org\/sigir2023\/wp-json\/wp\/v2\/media?parent=1987"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}