Understanding the mix-and-match relationships of fashion items receives increasing attention in fashion industry. Existing methods have primarily utilized the visual content to learn the visual compatibility and performed matching in a latent space. Despite their effectiveness, these methods work like a black box and cannot reveal the reasons that two items match well. The rich attributes associated with fashion items, e.g.,off-shoulder dress and black skinny jean, which describe the semantics of items in a human-interpretable way, have largely been ignored. This work tackles the interpretable fashion matching task, aiming to inject interpretability into the compatibility modeling of items. Specifically, given a corpus of matched pairs of items, we not only can predict the compatibility score of unseen pairs, but also learn the interpretable patterns that lead to a good match, e.g., white T-shirt matches with black trouser. We propose a new solution named A ttribute-based I nterpretable C ompatibility (AIC) method, which consists of three modules: 1) a tree-based module that extracts decision rules on matching prediction; 2) an embedding module that learns vector representation for a rule by accounting for the attribute semantics; and 3) a joint modeling module that unifies the visual embedding and rule embedding to predict the matching score. To justify our proposal, we contribute a new Lookastic dataset with fashion attributes available. Extensive experiments show that AIC not only outperforms several state-of-the-art methods, but also provides good interpretability on matching decisions.
{"title":"Interpretable Fashion Matching with Rich Attributes","authors":"Xun Yang, Xiangnan He, Xiang Wang, Yunshan Ma, Fuli Feng, Meng Wang, Tat-Seng Chua","doi":"10.1145/3331184.3331242","DOIUrl":"https://doi.org/10.1145/3331184.3331242","url":null,"abstract":"Understanding the mix-and-match relationships of fashion items receives increasing attention in fashion industry. Existing methods have primarily utilized the visual content to learn the visual compatibility and performed matching in a latent space. Despite their effectiveness, these methods work like a black box and cannot reveal the reasons that two items match well. The rich attributes associated with fashion items, e.g.,off-shoulder dress and black skinny jean, which describe the semantics of items in a human-interpretable way, have largely been ignored. This work tackles the interpretable fashion matching task, aiming to inject interpretability into the compatibility modeling of items. Specifically, given a corpus of matched pairs of items, we not only can predict the compatibility score of unseen pairs, but also learn the interpretable patterns that lead to a good match, e.g., white T-shirt matches with black trouser. We propose a new solution named A ttribute-based I nterpretable C ompatibility (AIC) method, which consists of three modules: 1) a tree-based module that extracts decision rules on matching prediction; 2) an embedding module that learns vector representation for a rule by accounting for the attribute semantics; and 3) a joint modeling module that unifies the visual embedding and rule embedding to predict the matching score. To justify our proposal, we contribute a new Lookastic dataset with fashion attributes available. Extensive experiments show that AIC not only outperforms several state-of-the-art methods, but also provides good interpretability on matching decisions.","PeriodicalId":20700,"journal":{"name":"Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval","volume":"15 3 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78521348","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}
Network embedding learns the vector representations of nodes. Most real world networks are heterogeneous and evolve over time. There are, however, no network embedding approaches designed for dynamic heterogeneous networks so far. Addressing this research gap is beneficial for analyzing and mining real world networks. We develop a novel representation learning method, change2vec, which considers a dynamic heterogeneous network as snapshots of networks with different time stamps. Instead of processing the whole network at each time stamp, change2vec models changes between two consecutive static networks by capturing newly-added and deleted nodes with their neighbour nodes as well as newly-formed or deleted edges that caused core structural changes known as triad closure or open processes. Change2vec leverages metapath based node embedding and change modeling to preserve both heterogeneous and dynamic features of a network. Experimental results show that change2vec outperforms two state-of-the-art methods in terms of clustering performance and efficiency.
{"title":"Network Embedding and Change Modeling in Dynamic Heterogeneous Networks","authors":"Ranran Bian, Yun Sing Koh, G. Dobbie, A. Divoli","doi":"10.1145/3331184.3331273","DOIUrl":"https://doi.org/10.1145/3331184.3331273","url":null,"abstract":"Network embedding learns the vector representations of nodes. Most real world networks are heterogeneous and evolve over time. There are, however, no network embedding approaches designed for dynamic heterogeneous networks so far. Addressing this research gap is beneficial for analyzing and mining real world networks. We develop a novel representation learning method, change2vec, which considers a dynamic heterogeneous network as snapshots of networks with different time stamps. Instead of processing the whole network at each time stamp, change2vec models changes between two consecutive static networks by capturing newly-added and deleted nodes with their neighbour nodes as well as newly-formed or deleted edges that caused core structural changes known as triad closure or open processes. Change2vec leverages metapath based node embedding and change modeling to preserve both heterogeneous and dynamic features of a network. Experimental results show that change2vec outperforms two state-of-the-art methods in terms of clustering performance and efficiency.","PeriodicalId":20700,"journal":{"name":"Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval","volume":"22 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73377666","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}
Xiaoli Wang, Rongzheng Wang, Z. Bao, Jiaying Liang, Wei Lu
Medical archives processing is a very important task in a medical information system. It generally consists of three steps: medical archives recognition, feature extraction and text classification. In this paper, we focus on empowering the medical archives processing with knowledge graphs. We first build a semantic-rich medical knowledge graph. Then, we recognize texts from medical archives using several popular optical character recognition (OCR) engines, and extract keywords from texts using a knowledge graph based feature extraction algorithm. Third, we define a semantic measure based on knowledge graph to evaluate the similarity between medical texts, and perform the text classification task. This measure can value semantic relatedness between medical documents, to enhance the text classification. We use medical archives collected from real hospitals for validation. The results show that our algorithms can significantly outperform typical baselines that employs only term statistics.
{"title":"Effective Medical Archives Processing Using Knowledge Graphs","authors":"Xiaoli Wang, Rongzheng Wang, Z. Bao, Jiaying Liang, Wei Lu","doi":"10.1145/3331184.3331350","DOIUrl":"https://doi.org/10.1145/3331184.3331350","url":null,"abstract":"Medical archives processing is a very important task in a medical information system. It generally consists of three steps: medical archives recognition, feature extraction and text classification. In this paper, we focus on empowering the medical archives processing with knowledge graphs. We first build a semantic-rich medical knowledge graph. Then, we recognize texts from medical archives using several popular optical character recognition (OCR) engines, and extract keywords from texts using a knowledge graph based feature extraction algorithm. Third, we define a semantic measure based on knowledge graph to evaluate the similarity between medical texts, and perform the text classification task. This measure can value semantic relatedness between medical documents, to enhance the text classification. We use medical archives collected from real hospitals for validation. The results show that our algorithms can significantly outperform typical baselines that employs only term statistics.","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":"80340519","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 developments in conversational IR have raised questions about the nature of interactions which occur between the user and the system and the cognitive capabilities expected of such systems. In our research, we investigate the completeness of existing theoretical frameworks in explaining conversational search data propose modifications to such systems. The linear and transient nature of speech makes it cognitively challenging for the user to process a large amount of information. We propose a study to evaluate the users' preference of modalities when using conversational search systems. The study will help us to understand how results should be presented in a conversational search system. As we observe how users search using audio queries, interact with the intermediary, and process the results presented, we aim to develop an insight on how to present results more efficiently in a conversational search setting. We also plan on exploring the effectiveness and consistency of different media in a conversational search setting. Our observations will inform future designs and help to create a better understanding of such systems.
{"title":"Informing the Design of Conversational IR Systems: Framework and Result Presentation","authors":"Souvick Ghosh","doi":"10.1145/3331184.3331422","DOIUrl":"https://doi.org/10.1145/3331184.3331422","url":null,"abstract":"Recent developments in conversational IR have raised questions about the nature of interactions which occur between the user and the system and the cognitive capabilities expected of such systems. In our research, we investigate the completeness of existing theoretical frameworks in explaining conversational search data propose modifications to such systems. The linear and transient nature of speech makes it cognitively challenging for the user to process a large amount of information. We propose a study to evaluate the users' preference of modalities when using conversational search systems. The study will help us to understand how results should be presented in a conversational search system. As we observe how users search using audio queries, interact with the intermediary, and process the results presented, we aim to develop an insight on how to present results more efficiently in a conversational search setting. We also plan on exploring the effectiveness and consistency of different media in a conversational search setting. Our observations will inform future designs and help to create a better understanding of such systems.","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":"82150495","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}
Linking phrases to knowledge base entities is a process known as entity linking and has already been widely explored for various content types such as tweets. A major step in entity linking is to recognize and/or classify phrases that can be disambiguated and linked to knowledge base entities, i.e., Named Entity Recognition and Classification. Unlike common entity recognition and linking systems, however, we aim to recognize, classify, and link entities which are implicitly mentioned, and hence lack a surface form, to appropriate knowledge base entries. In other words, the objective of our work is to recognize and identify core entities of a tweet when those entities are not explicitly mentioned; this process is referred to as Implicit Named Entity Recognition and Linking.
{"title":"Implicit Entity Recognition, Classification and Linking in Tweets","authors":"Hawre Hosseini","doi":"10.1145/3331184.3331416","DOIUrl":"https://doi.org/10.1145/3331184.3331416","url":null,"abstract":"Linking phrases to knowledge base entities is a process known as entity linking and has already been widely explored for various content types such as tweets. A major step in entity linking is to recognize and/or classify phrases that can be disambiguated and linked to knowledge base entities, i.e., Named Entity Recognition and Classification. Unlike common entity recognition and linking systems, however, we aim to recognize, classify, and link entities which are implicitly mentioned, and hence lack a surface form, to appropriate knowledge base entries. In other words, the objective of our work is to recognize and identify core entities of a tweet when those entities are not explicitly mentioned; this process is referred to as Implicit Named Entity Recognition and Linking.","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":"78968158","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 5B: Efficiency, Effectiveness and Performance","authors":"A. Trotman","doi":"10.1145/3349688","DOIUrl":"https://doi.org/10.1145/3349688","url":null,"abstract":"","PeriodicalId":20700,"journal":{"name":"Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval","volume":"17 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88417970","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 2B: Collaborative Filtering","authors":"I. Soboroff","doi":"10.1145/3349679","DOIUrl":"https://doi.org/10.1145/3349679","url":null,"abstract":"","PeriodicalId":20700,"journal":{"name":"Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval","volume":"142 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83900269","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}
Xu Chen, H. Chen, Hongteng Xu, Yongfeng Zhang, Yixin Cao, Zheng Qin, H. Zha
Fashion recommendation has attracted increasing attention from both industry and academic communities. This paper proposes a novel neural architecture for fashion recommendation based on both image region-level features and user review information. Our basic intuition is that: for a fashion image, not all the regions are equally important for the users, i.e., people usually care about a few parts of the fashion image. To model such human sense, we learn an attention model over many pre-segmented image regions, based on which we can understand where a user is really interested in on the image, and correspondingly, represent the image in a more accurate manner. In addition, by discovering such fine-grained visual preference, we can visually explain a recommendation by highlighting some regions of its image. For better learning the attention model, we also introduce user review information as a weak supervision signal to collect more comprehensive user preference. In our final framework, the visual and textual features are seamlessly coupled by a multimodal attention network. Based on this architecture, we can not only provide accurate recommendation, but also can accompany each recommended item with novel visual explanations. We conduct extensive experiments to demonstrate the superiority of our proposed model in terms of Top-N recommendation, and also we build a collectively labeled dataset for evaluating our provided visual explanations in a quantitative manner.
{"title":"Personalized Fashion Recommendation with Visual Explanations based on Multimodal Attention Network: Towards Visually Explainable Recommendation","authors":"Xu Chen, H. Chen, Hongteng Xu, Yongfeng Zhang, Yixin Cao, Zheng Qin, H. Zha","doi":"10.1145/3331184.3331254","DOIUrl":"https://doi.org/10.1145/3331184.3331254","url":null,"abstract":"Fashion recommendation has attracted increasing attention from both industry and academic communities. This paper proposes a novel neural architecture for fashion recommendation based on both image region-level features and user review information. Our basic intuition is that: for a fashion image, not all the regions are equally important for the users, i.e., people usually care about a few parts of the fashion image. To model such human sense, we learn an attention model over many pre-segmented image regions, based on which we can understand where a user is really interested in on the image, and correspondingly, represent the image in a more accurate manner. In addition, by discovering such fine-grained visual preference, we can visually explain a recommendation by highlighting some regions of its image. For better learning the attention model, we also introduce user review information as a weak supervision signal to collect more comprehensive user preference. In our final framework, the visual and textual features are seamlessly coupled by a multimodal attention network. Based on this architecture, we can not only provide accurate recommendation, but also can accompany each recommended item with novel visual explanations. We conduct extensive experiments to demonstrate the superiority of our proposed model in terms of Top-N recommendation, and also we build a collectively labeled dataset for evaluating our provided visual explanations in a quantitative manner.","PeriodicalId":20700,"journal":{"name":"Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval","volume":"9 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88838874","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}
Relational embedding methods encode objects and their relations as low-dimensional vectors. While achieving competitive performance on a variety of relational inference tasks, these methods fall short of preserving the hierarchies that are often formed in existing graph data, and ignore the rich edge attributes that describe the relation facts. In this paper, we propose a novel embedding method that simultaneously preserve the hierarchical property and the edge information in the edge-attributed relational hierarchies. The proposed method preserves the hierarchical relations by leveraging the non-linearity of hyperbolic vector translations, for which the edge attributes are exploited to capture the importance of each relation fact. Our experiment is conducted on the well-known Enron organizational chart, where the supervision relations between employees of the Enron company are accompanied with email-based attributes. We show that our method produces relational embeddings of higher quality than state-of-the-art methods, and outperforms a variety of strong baselines in reconstructing the organizational chart.
{"title":"Embedding Edge-attributed Relational Hierarchies","authors":"Muhao Chen, Chris Quirk","doi":"10.1145/3331184.3331278","DOIUrl":"https://doi.org/10.1145/3331184.3331278","url":null,"abstract":"Relational embedding methods encode objects and their relations as low-dimensional vectors. While achieving competitive performance on a variety of relational inference tasks, these methods fall short of preserving the hierarchies that are often formed in existing graph data, and ignore the rich edge attributes that describe the relation facts. In this paper, we propose a novel embedding method that simultaneously preserve the hierarchical property and the edge information in the edge-attributed relational hierarchies. The proposed method preserves the hierarchical relations by leveraging the non-linearity of hyperbolic vector translations, for which the edge attributes are exploited to capture the importance of each relation fact. Our experiment is conducted on the well-known Enron organizational chart, where the supervision relations between employees of the Enron company are accompanied with email-based attributes. We show that our method produces relational embeddings of higher quality than state-of-the-art methods, and outperforms a variety of strong baselines in reconstructing the organizational chart.","PeriodicalId":20700,"journal":{"name":"Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval","volume":"14 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85252707","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}
Digitized world demands data integration systems that combine data repositories from multiple data sources. Vast amounts of clinical and biomedical research data are considered a primary force enabling data-driven research toward advancing health research and for introducing efficiencies in healthcare delivery. Data-driven research may have many goals, including but not limited to improved diagnostics processes, novel biomedical discoveries, epidemiology, and education. However, finding and gaining access to relevant data remains an elusive goal. We identified these challenges and developed an Integrated Radiology Image Search (IRIS) framework that could be a step toward aiding data-driven research. We propose building data bridges to support retrieving ranked relevant documents from integrated repository. My research focuses on biomedical data integration and indexing systems and provide ranked document retrieval from an integrated repository. Though we currently focus on integrating biomedical data sources (for medical professionals), we believe that our proposed framework and methodologies can be used in other domains as well.
{"title":"Biomedical Heterogeneous Data Integration and Rank Retrieval using Data Bridges","authors":"P. Deshpande","doi":"10.1145/3331184.3331417","DOIUrl":"https://doi.org/10.1145/3331184.3331417","url":null,"abstract":"Digitized world demands data integration systems that combine data repositories from multiple data sources. Vast amounts of clinical and biomedical research data are considered a primary force enabling data-driven research toward advancing health research and for introducing efficiencies in healthcare delivery. Data-driven research may have many goals, including but not limited to improved diagnostics processes, novel biomedical discoveries, epidemiology, and education. However, finding and gaining access to relevant data remains an elusive goal. We identified these challenges and developed an Integrated Radiology Image Search (IRIS) framework that could be a step toward aiding data-driven research. We propose building data bridges to support retrieving ranked relevant documents from integrated repository. My research focuses on biomedical data integration and indexing systems and provide ranked document retrieval from an integrated repository. Though we currently focus on integrating biomedical data sources (for medical professionals), we believe that our proposed framework and methodologies can be used in other domains as well.","PeriodicalId":20700,"journal":{"name":"Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval","volume":"53 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90948499","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：