Fairness and related concerns have become of increasing importance in a variety of AI and machine learning contexts. They are also highly relevant to information retrieval and related problems such as recommendation, as evidenced by the growing literature in SIGIR, FAT*, RecSys, and special sessions such as the FATREC workshop and the Fairness track at TREC 2019; however, translating algorithmic fairness constructs from classification, scoring, and even many ranking settings into information retrieval and recommendation scenarios is not a straightforward task. This tutorial will help to orient IR researchers to algorithmic fairness, understand how concepts do and do not translate from other settings, and provide an introduction to the growing literature on this topic.
{"title":"Fairness and Discrimination in Retrieval and Recommendation","authors":"Michael D. Ekstrand, R. Burke, Fernando Diaz","doi":"10.1145/3331184.3331380","DOIUrl":"https://doi.org/10.1145/3331184.3331380","url":null,"abstract":"Fairness and related concerns have become of increasing importance in a variety of AI and machine learning contexts. They are also highly relevant to information retrieval and related problems such as recommendation, as evidenced by the growing literature in SIGIR, FAT*, RecSys, and special sessions such as the FATREC workshop and the Fairness track at TREC 2019; however, translating algorithmic fairness constructs from classification, scoring, and even many ranking settings into information retrieval and recommendation scenarios is not a straightforward task. This tutorial will help to orient IR researchers to algorithmic fairness, understand how concepts do and do not translate from other settings, and provide an introduction to the growing literature on this topic.","PeriodicalId":20700,"journal":{"name":"Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89964269","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"TDP","authors":"Zhenlong Zhu, Ruixuan Li, Minghui Shan, Yuhua Li, Lu Gao, Fei Wang, Jixing Xu, X. Gu","doi":"10.1007/978-3-662-48986-4_313617","DOIUrl":"https://doi.org/10.1007/978-3-662-48986-4_313617","url":null,"abstract":"","PeriodicalId":20700,"journal":{"name":"Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval","volume":"19 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89675349","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Yukun Zheng, Jiaxin Mao, Yiqun Liu, Zixin Ye, Min Zhang, Shaoping Ma
Machine Reading Comprehension (MRC) is one of the most challenging tasks in both NLP and IR researches. Recently, a number of deep neural models have been successfully adopted to some simplified MRC task settings, whose performances were close to or even better than human beings. However, these models still have large performance gaps with human beings in more practical settings, such as MS MARCO and DuReader datasets. Although there are many works studying human reading behavior, the behavior patterns in complex reading comprehension scenarios remain under-investigated. We believe that a better understanding of how human reads and allocates their attention during reading comprehension processes can help improve the performance of MRC tasks. In this paper, we conduct a lab study to investigate human's reading behavior patterns during reading comprehension tasks, where 32 users are recruited to take 60 distinct tasks. By analyzing the collected eye-tracking data and answers from participants, we propose a two-stage reading behavior model, in which the first stage is to search for possible answer candidates and the second stage is to generate the final answer through a comparison and verification process. We also find that human's attention distribution is affected by both question-dependent factors (e.g., answer and soft matching signal with questions) and question-independent factors (e.g., position, IDF and Part-of-Speech tags of words). We extract features derived from the two-stage reading behavior model to predict human's attention signals during reading comprehension, which significantly improves performance in the MRC task. Findings in our work may bring insight into the understanding of human reading and information seeking processes, and help the machine to better meet users' information needs.
{"title":"Human Behavior Inspired Machine Reading Comprehension","authors":"Yukun Zheng, Jiaxin Mao, Yiqun Liu, Zixin Ye, Min Zhang, Shaoping Ma","doi":"10.1145/3331184.3331231","DOIUrl":"https://doi.org/10.1145/3331184.3331231","url":null,"abstract":"Machine Reading Comprehension (MRC) is one of the most challenging tasks in both NLP and IR researches. Recently, a number of deep neural models have been successfully adopted to some simplified MRC task settings, whose performances were close to or even better than human beings. However, these models still have large performance gaps with human beings in more practical settings, such as MS MARCO and DuReader datasets. Although there are many works studying human reading behavior, the behavior patterns in complex reading comprehension scenarios remain under-investigated. We believe that a better understanding of how human reads and allocates their attention during reading comprehension processes can help improve the performance of MRC tasks. In this paper, we conduct a lab study to investigate human's reading behavior patterns during reading comprehension tasks, where 32 users are recruited to take 60 distinct tasks. By analyzing the collected eye-tracking data and answers from participants, we propose a two-stage reading behavior model, in which the first stage is to search for possible answer candidates and the second stage is to generate the final answer through a comparison and verification process. We also find that human's attention distribution is affected by both question-dependent factors (e.g., answer and soft matching signal with questions) and question-independent factors (e.g., position, IDF and Part-of-Speech tags of words). We extract features derived from the two-stage reading behavior model to predict human's attention signals during reading comprehension, which significantly improves performance in the MRC task. Findings in our work may bring insight into the understanding of human reading and information seeking processes, and help the machine to better meet users' information needs.","PeriodicalId":20700,"journal":{"name":"Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval","volume":"40 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84858261","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Stuart Mackie, David Macdonald, L. Azzopardi, Yashar Moshfeghi
Procurement legislation stipulates that information about the goods, services, or works, that tax-funded authorities wish to purchase are made publicly available in a procurement contract notice. However, for businesses wishing to tender for such competitive opportunities, finding relevant procurement contract notices presents a challenging professional search task. In this talk, we will provide an overview of procurement search and then describe the challenges in addressing the related search and recommendation tasks.
{"title":"Looking for Opportunities: Challenges in Procurement Search","authors":"Stuart Mackie, David Macdonald, L. Azzopardi, Yashar Moshfeghi","doi":"10.1145/3331184.3331428","DOIUrl":"https://doi.org/10.1145/3331184.3331428","url":null,"abstract":"Procurement legislation stipulates that information about the goods, services, or works, that tax-funded authorities wish to purchase are made publicly available in a procurement contract notice. However, for businesses wishing to tender for such competitive opportunities, finding relevant procurement contract notices presents a challenging professional search task. In this talk, we will provide an overview of procurement search and then describe the challenges in addressing the related search and recommendation tasks.","PeriodicalId":20700,"journal":{"name":"Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval","volume":"15 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75236337","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Di Liang, Fubao Zhang, Weidong Zhang, Qi Zhang, Jinlan Fu, Minlong Peng, Tao Gui, Xuanjing Huang
Community-based question answering (CQA), which provides a platform for people with diverse backgrounds to share information and knowledge, has become increasingly popular. With the accumulation of site data, methods to detect duplicate questions in CQA sites have attracted considerable attention. Existing methods typically use only questions to complete the task. However, the paired answers may also provide valuable information. In this paper, we propose an answer information- enhanced adaptive multi-attention network (AMAN) to perform this task. AMAN takes full advantage of the semantic information in the paired answers while alleviating the noise problem caused by adding the answers. To evaluate the proposed method, we use a CQADupStack set and the Quora question-pair dataset expanded with paired answers. Experimental results demonstrate that the proposed model can achieve state-of-the-art performance on the above two data sets.
{"title":"Adaptive Multi-Attention Network Incorporating Answer Information for Duplicate Question Detection","authors":"Di Liang, Fubao Zhang, Weidong Zhang, Qi Zhang, Jinlan Fu, Minlong Peng, Tao Gui, Xuanjing Huang","doi":"10.1145/3331184.3331228","DOIUrl":"https://doi.org/10.1145/3331184.3331228","url":null,"abstract":"Community-based question answering (CQA), which provides a platform for people with diverse backgrounds to share information and knowledge, has become increasingly popular. With the accumulation of site data, methods to detect duplicate questions in CQA sites have attracted considerable attention. Existing methods typically use only questions to complete the task. However, the paired answers may also provide valuable information. In this paper, we propose an answer information- enhanced adaptive multi-attention network (AMAN) to perform this task. AMAN takes full advantage of the semantic information in the paired answers while alleviating the noise problem caused by adding the answers. To evaluate the proposed method, we use a CQADupStack set and the Quora question-pair dataset expanded with paired answers. Experimental results demonstrate that the proposed model can achieve state-of-the-art performance on the above two data sets.","PeriodicalId":20700,"journal":{"name":"Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval","volume":"68 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76262410","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The query performance prediction task (QPP) is estimating retrieval effectiveness in the absence of relevance judgments. Prior work has focused on prediction for retrieval methods based on surface level query-document similarities (e.g., query likelihood). We address the prediction challenge for pseudo-feedback-based retrieval methods which utilize an initial retrieval to induce a new query model; the query model is then used for a second (final) retrieval. Our suggested approach accounts for the presumed effectiveness of the initially retrieved list, its similarity with the final retrieved list and properties of the latter. Empirical evaluation demonstrates the clear merits of our approach.
{"title":"Query Performance Prediction for Pseudo-Feedback-Based Retrieval","authors":"Haggai Roitman, Oren Kurland","doi":"10.1145/3331184.3331369","DOIUrl":"https://doi.org/10.1145/3331184.3331369","url":null,"abstract":"The query performance prediction task (QPP) is estimating retrieval effectiveness in the absence of relevance judgments. Prior work has focused on prediction for retrieval methods based on surface level query-document similarities (e.g., query likelihood). We address the prediction challenge for pseudo-feedback-based retrieval methods which utilize an initial retrieval to induce a new query model; the query model is then used for a second (final) retrieval. Our suggested approach accounts for the presumed effectiveness of the initially retrieved list, its similarity with the final retrieved list and properties of the latter. Empirical evaluation demonstrates the clear merits of our approach.","PeriodicalId":20700,"journal":{"name":"Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval","volume":"69 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75649309","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
R. Clancy, N. Ferro, C. Hauff, Jimmy J. Lin, T. Sakai, Z. Z. Wu
The importance of repeatability, replicability, and reproducibility is broadly recognized in the computational sciences, both in supporting desirable scientific methodology as well as sustaining empirical progress. This workshop tackles the replicability challenge for ad hoc document retrieval, via a common Docker interface specification to support images that capture systems performing ad hoc retrieval experiments on standard test collections.
{"title":"The SIGIR 2019 Open-Source IR Replicability Challenge (OSIRRC 2019)","authors":"R. Clancy, N. Ferro, C. Hauff, Jimmy J. Lin, T. Sakai, Z. Z. Wu","doi":"10.1145/3331184.3331647","DOIUrl":"https://doi.org/10.1145/3331184.3331647","url":null,"abstract":"The importance of repeatability, replicability, and reproducibility is broadly recognized in the computational sciences, both in supporting desirable scientific methodology as well as sustaining empirical progress. This workshop tackles the replicability challenge for ad hoc document retrieval, via a common Docker interface specification to support images that capture systems performing ad hoc retrieval experiments on standard test collections.","PeriodicalId":20700,"journal":{"name":"Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval","volume":"136 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75690216","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
F. Xiao, Zhen Wang, Haikuan Huang, Jun Huang, Xi Chen, Hongbo Deng, Minghui Qiu, Xiaoli Gong
Online shopping has been a habit of more and more people, while most users are unable to craft an informative query, and thus it often takes a long search session to satisfy their purchase intents. We present AliISA - a shopping assistant which offers users some tips to further specify their queries during a search session. With such an interactive search, users tend to find targeted items with fewer page requests, which often means a better user experience. Currently, AliISA assists tens of millions of users per day, earns more usage than existing systems, and consequently brings in a 5% improvement in CVR. In this paper, we present our system, describe the underlying techniques, and discuss our experience in stabilizing reinforcement learning under an E-commerce environment.
{"title":"AliISA","authors":"F. Xiao, Zhen Wang, Haikuan Huang, Jun Huang, Xi Chen, Hongbo Deng, Minghui Qiu, Xiaoli Gong","doi":"10.1145/3331184.3331409","DOIUrl":"https://doi.org/10.1145/3331184.3331409","url":null,"abstract":"Online shopping has been a habit of more and more people, while most users are unable to craft an informative query, and thus it often takes a long search session to satisfy their purchase intents. We present AliISA - a shopping assistant which offers users some tips to further specify their queries during a search session. With such an interactive search, users tend to find targeted items with fewer page requests, which often means a better user experience. Currently, AliISA assists tens of millions of users per day, earns more usage than existing systems, and consequently brings in a 5% improvement in CVR. In this paper, we present our system, describe the underlying techniques, and discuss our experience in stabilizing reinforcement learning under an E-commerce environment.","PeriodicalId":20700,"journal":{"name":"Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval","volume":"45 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72840056","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The Web contains many APIs that could be combined in countless ways to enable Intelligent Assistants to complete all sorts of tasks. We propose a method to automatically produce task completion flows from a collection of these APIs by combining them in a graph and automatically extracting paths from the graph for task completion. These paths chain together API calls and use the output of executed APIs as inputs to others. We automatically extract these paths from an API graph in response to a user query and then rank the paths by the likelihood of them leading to user satisfaction. We apply our approach for task completion in the email and calendar domains and show how it can be used to automatically create task completion flows.
{"title":"Automatic Task Completion Flows from Web APIs","authors":"Kyle Williams, Seyyed Hadi Hashemi, I. Zitouni","doi":"10.1145/3331184.3331318","DOIUrl":"https://doi.org/10.1145/3331184.3331318","url":null,"abstract":"The Web contains many APIs that could be combined in countless ways to enable Intelligent Assistants to complete all sorts of tasks. We propose a method to automatically produce task completion flows from a collection of these APIs by combining them in a graph and automatically extracting paths from the graph for task completion. These paths chain together API calls and use the output of executed APIs as inputs to others. We automatically extract these paths from an API graph in response to a user query and then rank the paths by the likelihood of them leading to user satisfaction. We apply our approach for task completion in the email and calendar domains and show how it can be used to automatically create task completion flows.","PeriodicalId":20700,"journal":{"name":"Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval","volume":"100 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72559923","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Recent studies have shown that incorporating users' reviews into the collaborative filtering strategy can significantly boost the recommendation accuracy. A pressing challenge resides on learning how reviews influence users' rating behaviors. In this paper, we propose an Adversarial Training approach for Review-based recommendations, namely ATR. We design a neural architecture of sequence-to-sequence learning to calculate the deep representations of users' reviews on items following an adversarial training strategy. At the same time we jointly learn to factorize the rating matrix, by regularizing the deep representations of reviews with the user and item latent features. In doing so, our model captures the non-linear associations among reviews and ratings while producing a review for each user-item pair. Our experiments on publicly available datasets demonstrate the effectiveness of the proposed model, outperforming other state-of-the-art methods.
{"title":"Adversarial Training for Review-Based Recommendations","authors":"Dimitrios Rafailidis, F. Crestani","doi":"10.1145/3331184.3331313","DOIUrl":"https://doi.org/10.1145/3331184.3331313","url":null,"abstract":"Recent studies have shown that incorporating users' reviews into the collaborative filtering strategy can significantly boost the recommendation accuracy. A pressing challenge resides on learning how reviews influence users' rating behaviors. In this paper, we propose an Adversarial Training approach for Review-based recommendations, namely ATR. We design a neural architecture of sequence-to-sequence learning to calculate the deep representations of users' reviews on items following an adversarial training strategy. At the same time we jointly learn to factorize the rating matrix, by regularizing the deep representations of reviews with the user and item latent features. In doing so, our model captures the non-linear associations among reviews and ratings while producing a review for each user-item pair. Our experiments on publicly available datasets demonstrate the effectiveness of the proposed model, outperforming other state-of-the-art methods.","PeriodicalId":20700,"journal":{"name":"Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval","volume":"80 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72670476","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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