Explainable recommendation and search attempt to develop models or methods that not only generate high-quality recommendation or search results, but also interpretability of the models or explanations of the results for users or system designers, which can help to improve the system transparency, persuasiveness, trustworthiness, and effectiveness, etc. This is even more important in personalized search and recommendation scenarios, where users would like to know why a particular product, web page, news report, or friend suggestion exists in his or her own search and recommendation lists. The workshop focuses on the research and application of explainable recommendation, search, and a broader scope of IR tasks. It will gather researchers as well as practitioners in the field for discussions, idea communications, and research promotions. It will also generate insightful debates about the recent regulations regarding AI interpretability, to a broader community including but not limited to IR, machine learning, AI, Data Science, and beyond.
{"title":"EARS 2019: The 2nd International Workshop on ExplainAble Recommendation and Search","authors":"Yongfeng Zhang, Yi Zhang, Min Zhang, C. Shah","doi":"10.1145/3331184.3331649","DOIUrl":"https://doi.org/10.1145/3331184.3331649","url":null,"abstract":"Explainable recommendation and search attempt to develop models or methods that not only generate high-quality recommendation or search results, but also interpretability of the models or explanations of the results for users or system designers, which can help to improve the system transparency, persuasiveness, trustworthiness, and effectiveness, etc. This is even more important in personalized search and recommendation scenarios, where users would like to know why a particular product, web page, news report, or friend suggestion exists in his or her own search and recommendation lists. The workshop focuses on the research and application of explainable recommendation, search, and a broader scope of IR tasks. It will gather researchers as well as practitioners in the field for discussions, idea communications, and research promotions. It will also generate insightful debates about the recent regulations regarding AI interpretability, to a broader community including but not limited to IR, machine learning, AI, Data Science, and beyond.","PeriodicalId":20700,"journal":{"name":"Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval","volume":"35 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74080469","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}
Discriminative learning algorithms such as logistic regression excel when training data are plentiful, but falter when it is meager. An extreme case is text retrieval (zero training data), where discriminative learning is impossible and heuristics such as BM25, which combine domain knowledge (a topical keyword query) with generative learning (Naive Bayes), are dominant. Building on past work, we show that BM25-inspired Gaussian priors for Bayesian logistic regression based on topical keywords provide better effectiveness than the usual L2 (zero mode, uniform variance) Gaussian prior. On two high recall retrieval datasets, the resulting models transition smoothly from BM25 level effectiveness to discriminative effectiveness as training data volume increases, dominating L2 regularization even when substantial training data is available.
{"title":"Text Retrieval Priors for Bayesian Logistic Regression","authors":"Eugene Yang, D. Lewis, O. Frieder","doi":"10.1145/3331184.3331299","DOIUrl":"https://doi.org/10.1145/3331184.3331299","url":null,"abstract":"Discriminative learning algorithms such as logistic regression excel when training data are plentiful, but falter when it is meager. An extreme case is text retrieval (zero training data), where discriminative learning is impossible and heuristics such as BM25, which combine domain knowledge (a topical keyword query) with generative learning (Naive Bayes), are dominant. Building on past work, we show that BM25-inspired Gaussian priors for Bayesian logistic regression based on topical keywords provide better effectiveness than the usual L2 (zero mode, uniform variance) Gaussian prior. On two high recall retrieval datasets, the resulting models transition smoothly from BM25 level effectiveness to discriminative effectiveness as training data volume increases, dominating L2 regularization even when substantial training data is available.","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":"84808093","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}
Gayle McElvain, George Sanchez, S. Matthews, Don Teo, Filippo Pompili, Tonya Custis
We present a non-factoid QA system that provides legally accurate, jurisdictionally relevant, and conversationally responsive answers to user-entered questions in the legal domain. This commercially available system is entirely based on NLP and IR, and does not rely on a structured knowledge base. WestSearch Plus aims to provide concise one sentence answers for basic questions about the law. It is not restricted in scope to particular topics or jurisdictions. The corpus of potential answers contains approximately 22M documents classified to over 120K legal topics.
{"title":"WestSearch Plus: A Non-factoid Question-Answering System for the Legal Domain","authors":"Gayle McElvain, George Sanchez, S. Matthews, Don Teo, Filippo Pompili, Tonya Custis","doi":"10.1145/3331184.3331397","DOIUrl":"https://doi.org/10.1145/3331184.3331397","url":null,"abstract":"We present a non-factoid QA system that provides legally accurate, jurisdictionally relevant, and conversationally responsive answers to user-entered questions in the legal domain. This commercially available system is entirely based on NLP and IR, and does not rely on a structured knowledge base. WestSearch Plus aims to provide concise one sentence answers for basic questions about the law. It is not restricted in scope to particular topics or jurisdictions. The corpus of potential answers contains approximately 22M documents classified to over 120K legal topics.","PeriodicalId":20700,"journal":{"name":"Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval","volume":"33 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85030557","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}
X. Lu, Lei Zhu, Zhiyong Cheng, Liqiang Nie, Huaxiang Zhang
Multi-modal hashing is an effective technique to support large-scale multimedia retrieval, due to its capability of encoding heterogeneous multi-modal features into compact and similarity-preserving binary codes. Although great progress has been achieved so far, existing methods still suffer from several problems, including: 1) All existing methods simply adopt fixed modality combination weights in online hashing process to generate the query hash codes. This strategy cannot adaptively capture the variations of different queries. 2) They either suffer from insufficient semantics (for unsupervised methods) or require high computation and storage cost (for the supervised methods, which rely on pair-wise semantic matrix). 3) They solve the hash codes with relaxed optimization strategy or bit-by-bit discrete optimization, which results in significant quantization loss or consumes considerable computation time. To address the above limitations, in this paper, we propose an Online Multi-modal Hashing with Dynamic Query-adaption (OMH-DQ) method in a novel fashion. Specifically, a self-weighted fusion strategy is designed to adaptively preserve the multi-modal feature information into hash codes by exploiting their complementarity. The hash codes are learned with the supervision of pair-wise semantic labels to enhance their discriminative capability, while avoiding the challenging symmetric similarity matrix factorization. Under such learning framework, the binary hash codes can be directly obtained with efficient operations and without quantization errors. Accordingly, our method can benefit from the semantic labels, and simultaneously, avoid the high computation complexity. Moreover, to accurately capture the query variations, at the online retrieval stage, we design a parameter-free online hashing module which can adaptively learn the query hash codes according to the dynamic query contents. Extensive experiments demonstrate the state-of-the-art performance of the proposed approach from various aspects.
{"title":"Online Multi-modal Hashing with Dynamic Query-adaption","authors":"X. Lu, Lei Zhu, Zhiyong Cheng, Liqiang Nie, Huaxiang Zhang","doi":"10.1145/3331184.3331217","DOIUrl":"https://doi.org/10.1145/3331184.3331217","url":null,"abstract":"Multi-modal hashing is an effective technique to support large-scale multimedia retrieval, due to its capability of encoding heterogeneous multi-modal features into compact and similarity-preserving binary codes. Although great progress has been achieved so far, existing methods still suffer from several problems, including: 1) All existing methods simply adopt fixed modality combination weights in online hashing process to generate the query hash codes. This strategy cannot adaptively capture the variations of different queries. 2) They either suffer from insufficient semantics (for unsupervised methods) or require high computation and storage cost (for the supervised methods, which rely on pair-wise semantic matrix). 3) They solve the hash codes with relaxed optimization strategy or bit-by-bit discrete optimization, which results in significant quantization loss or consumes considerable computation time. To address the above limitations, in this paper, we propose an Online Multi-modal Hashing with Dynamic Query-adaption (OMH-DQ) method in a novel fashion. Specifically, a self-weighted fusion strategy is designed to adaptively preserve the multi-modal feature information into hash codes by exploiting their complementarity. The hash codes are learned with the supervision of pair-wise semantic labels to enhance their discriminative capability, while avoiding the challenging symmetric similarity matrix factorization. Under such learning framework, the binary hash codes can be directly obtained with efficient operations and without quantization errors. Accordingly, our method can benefit from the semantic labels, and simultaneously, avoid the high computation complexity. Moreover, to accurately capture the query variations, at the online retrieval stage, we design a parameter-free online hashing module which can adaptively learn the query hash codes according to the dynamic query contents. Extensive experiments demonstrate the state-of-the-art performance of the proposed approach from various aspects.","PeriodicalId":20700,"journal":{"name":"Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval","volume":"115 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79398273","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":"Session details: Session 8C: Summarization and Information Extraction","authors":"Bárbara Poblete","doi":"10.1145/3349696","DOIUrl":"https://doi.org/10.1145/3349696","url":null,"abstract":"","PeriodicalId":20700,"journal":{"name":"Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval","volume":"41 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80709819","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}
Daoyuan Chen, Min Yang, Haitao Zheng, Yaliang Li, Ying Shen
Knowledge Based Question Answering (KBQA) is one of the most promising approaches to provide suitable answers for the queries posted by users. Relation detection that aims to take full advantage of the substantial knowledge contained in knowledge base (KB) becomes increasingly important. Significant progress has been made in performing relation detection over KB. However, recent deep neural networks that achieve the state of the art on KB-based relation detection task only consider the context information of question sentences rather than the relatedness between question and answer candidates, and exclusively extract the relation from KB triple rather than learn informative relational path. In this paper, we propose a Knowledge-driven Relation Detection network (KRD) to interactively learn answer-enhanced question representations and path-aware relation representations for relation detection. A Siamese LSTM is employed into a similarity matching process between the question representation and relation representation. Experimental results on the SimpleQuestions and WebQSP datasets demonstrate that KRD outperforms the state-of-the-art methods. In addition, a series of ablation test show the robust superiority of the proposed method.
{"title":"Answer-enhanced Path-aware Relation Detection over Knowledge Base","authors":"Daoyuan Chen, Min Yang, Haitao Zheng, Yaliang Li, Ying Shen","doi":"10.1145/3331184.3331328","DOIUrl":"https://doi.org/10.1145/3331184.3331328","url":null,"abstract":"Knowledge Based Question Answering (KBQA) is one of the most promising approaches to provide suitable answers for the queries posted by users. Relation detection that aims to take full advantage of the substantial knowledge contained in knowledge base (KB) becomes increasingly important. Significant progress has been made in performing relation detection over KB. However, recent deep neural networks that achieve the state of the art on KB-based relation detection task only consider the context information of question sentences rather than the relatedness between question and answer candidates, and exclusively extract the relation from KB triple rather than learn informative relational path. In this paper, we propose a Knowledge-driven Relation Detection network (KRD) to interactively learn answer-enhanced question representations and path-aware relation representations for relation detection. A Siamese LSTM is employed into a similarity matching process between the question representation and relation representation. Experimental results on the SimpleQuestions and WebQSP datasets demonstrate that KRD outperforms the state-of-the-art methods. In addition, a series of ablation test show the robust superiority of the proposed method.","PeriodicalId":20700,"journal":{"name":"Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval","volume":"70 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77720921","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 spread of false information on the Web is one of the main problems of our society. Automatic detection of fake news posts is a hard task since they are intentionally written to mislead the readers and to trigger intense emotions to them in an attempt to be disseminated in the social networks. Even though recent studies have explored different linguistic patterns of false claims, the role of emotional signals has not yet been explored. In this paper, we study the role of emotional signals in fake news detection. In particular, we propose an LSTM model that incorporates emotional signals extracted from the text of the claims to differentiate between credible and non-credible ones. Experiments on real world datasets show the importance of emotional signals for credibility assessment.
{"title":"Leveraging Emotional Signals for Credibility Detection","authors":"Anastasia Giahanou, Paolo Rosso, F. Crestani","doi":"10.1145/3331184.3331285","DOIUrl":"https://doi.org/10.1145/3331184.3331285","url":null,"abstract":"The spread of false information on the Web is one of the main problems of our society. Automatic detection of fake news posts is a hard task since they are intentionally written to mislead the readers and to trigger intense emotions to them in an attempt to be disseminated in the social networks. Even though recent studies have explored different linguistic patterns of false claims, the role of emotional signals has not yet been explored. In this paper, we study the role of emotional signals in fake news detection. In particular, we propose an LSTM model that incorporates emotional signals extracted from the text of the claims to differentiate between credible and non-credible ones. Experiments on real world datasets show the importance of emotional signals for credibility assessment.","PeriodicalId":20700,"journal":{"name":"Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval","volume":"5 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81399727","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}
Rui Li, Yunjiang Jiang, Wen-Yun Yang, Guoyu Tang, Songlin Wang, Chaoyi Ma, Wei He, Xi Xiong, Yun Xiao, Y. Zhao
We introduce deep learning models to the two most important stages in product search at JD.com, one of the largest e-commerce platforms in the world. Specifically, we outline the design of a deep learning system that retrieves semantically relevant items to a query within milliseconds, and a pairwise deep re-ranking system, which learns subtle user preferences. Compared to traditional search systems, the proposed approaches are better at semantic retrieval and personalized ranking, achieving significant improvements.
{"title":"From Semantic Retrieval to Pairwise Ranking: Applying Deep Learning in E-commerce Search","authors":"Rui Li, Yunjiang Jiang, Wen-Yun Yang, Guoyu Tang, Songlin Wang, Chaoyi Ma, Wei He, Xi Xiong, Yun Xiao, Y. Zhao","doi":"10.1145/3331184.3331434","DOIUrl":"https://doi.org/10.1145/3331184.3331434","url":null,"abstract":"We introduce deep learning models to the two most important stages in product search at JD.com, one of the largest e-commerce platforms in the world. Specifically, we outline the design of a deep learning system that retrieves semantically relevant items to a query within milliseconds, and a pairwise deep re-ranking system, which learns subtle user preferences. Compared to traditional search systems, the proposed approaches are better at semantic retrieval and personalized ranking, achieving significant improvements.","PeriodicalId":20700,"journal":{"name":"Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval","volume":"2008 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82575146","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}
Question answering from tables (TableQA) extracting answers from tables from the question given in natural language, has been actively studied. Existing models have been trained and evaluated mostly with respect to answer accuracy using public benchmark datasets such as WikiSQL. The goal of this demonstration is to show a debugging tool for such models, explaining answers to humans, known as explanatory debugging. Our key distinction is making it "actionable" to allow users to directly correct models upon explanation. Specifically, our tool surfaces annotation and models errors for users to correct, and provides actionable insights.
{"title":"Explanatory and Actionable Debugging for Machine Learning: A TableQA Demonstration","authors":"Minseok Cho, Gyeongbok Lee, Seung-won Hwang","doi":"10.1145/3331184.3331404","DOIUrl":"https://doi.org/10.1145/3331184.3331404","url":null,"abstract":"Question answering from tables (TableQA) extracting answers from tables from the question given in natural language, has been actively studied. Existing models have been trained and evaluated mostly with respect to answer accuracy using public benchmark datasets such as WikiSQL. The goal of this demonstration is to show a debugging tool for such models, explaining answers to humans, known as explanatory debugging. Our key distinction is making it \"actionable\" to allow users to directly correct models upon explanation. Specifically, our tool surfaces annotation and models errors for users to correct, and provides actionable insights.","PeriodicalId":20700,"journal":{"name":"Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval","volume":"27 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89516279","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}
Document representation is the core of many NLP tasks on machine understanding. A general representation learned in an unsupervised manner reserves generality and can be used for various applications. In practice, sentiment analysis (SA) has been a challenging task that is regarded to be deeply semantic-related and is often used to assess general representations. Existing methods on unsupervised document representation learning can be separated into two families: sequential ones, which explicitly take the ordering of words into consideration, and non-sequential ones, which do not explicitly do so. However, both of them suffer from their own weaknesses. In this paper, we propose a model that overcomes difficulties encountered by both families of methods. Experiments show that our model outperforms state-of-the-art methods on popular SA datasets and a fine-grained aspect-based SA by a large margin.
{"title":"Learning Unsupervised Semantic Document Representation for Fine-grained Aspect-based Sentiment Analysis","authors":"Hao-Ming Fu, Pu-Jen Cheng","doi":"10.1145/3331184.3331320","DOIUrl":"https://doi.org/10.1145/3331184.3331320","url":null,"abstract":"Document representation is the core of many NLP tasks on machine understanding. A general representation learned in an unsupervised manner reserves generality and can be used for various applications. In practice, sentiment analysis (SA) has been a challenging task that is regarded to be deeply semantic-related and is often used to assess general representations. Existing methods on unsupervised document representation learning can be separated into two families: sequential ones, which explicitly take the ordering of words into consideration, and non-sequential ones, which do not explicitly do so. However, both of them suffer from their own weaknesses. In this paper, we propose a model that overcomes difficulties encountered by both families of methods. Experiments show that our model outperforms state-of-the-art methods on popular SA datasets and a fine-grained aspect-based SA by a large margin.","PeriodicalId":20700,"journal":{"name":"Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval","volume":"115 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89849204","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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