Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval最新文献_第2页

Interpretable Fashion Matching with Rich Attributes 具有丰富属性的可解释时尚匹配

Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval Pub Date : 2019-07-18 DOI: 10.1145/3331184.3331242

Xun Yang, Xiangnan He, Xiang Wang, Yunshan Ma, Fuli Feng, Meng Wang, Tat-Seng Chua

{"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":null,"pages":null},"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}

引用次数: 69

Network Embedding and Change Modeling in Dynamic Heterogeneous Networks 动态异构网络中的网络嵌入与变化建模

Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval Pub Date : 2019-07-18 DOI: 10.1145/3331184.3331273

Ranran Bian, Yun Sing Koh, G. Dobbie, A. Divoli

引用次数: 47

Effective Medical Archives Processing Using Knowledge Graphs 利用知识图谱有效地处理医疗档案

Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval Pub Date : 2019-07-18 DOI: 10.1145/3331184.3331350

Xiaoli Wang, Rongzheng Wang, Z. Bao, Jiaying Liang, Wei Lu

引用次数: 7

Informing the Design of Conversational IR Systems: Framework and Result Presentation 会话式IR系统的设计:框架与结果呈现

Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval Pub Date : 2019-07-18 DOI: 10.1145/3331184.3331422

Souvick Ghosh

{"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":null,"pages":null},"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}

引用次数: 6

Implicit Entity Recognition, Classification and Linking in Tweets 推文中的隐式实体识别、分类和链接

Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval Pub Date : 2019-07-18 DOI: 10.1145/3331184.3331416

Hawre Hosseini

引用次数: 9

Session details: Session 5B: Efficiency, Effectiveness and Performance 会议详情:会议5B:效率，有效性和绩效

Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval Pub Date : 2019-07-18 DOI: 10.1145/3349688

A. Trotman

引用次数: 0

Session details: Session 2B: Collaborative Filtering 会话详细信息:会话2B:协同过滤

Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval Pub Date : 2019-07-18 DOI: 10.1145/3349679

I. Soboroff

引用次数: 0

Personalized Fashion Recommendation with Visual Explanations based on Multimodal Attention Network: Towards Visually Explainable Recommendation 基于多模态注意网络的个性化视觉解释时尚推荐:面向视觉可解释推荐

Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval Pub Date : 2019-07-18 DOI: 10.1145/3331184.3331254

Xu Chen, H. Chen, Hongteng Xu, Yongfeng Zhang, Yixin Cao, Zheng Qin, H. Zha

{"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":null,"pages":null},"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}

引用次数: 176

Embedding Edge-attributed Relational Hierarchies 嵌入边缘属性的关系层次结构

Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval Pub Date : 2019-07-18 DOI: 10.1145/3331184.3331278

Muhao Chen, Chris Quirk

引用次数: 14

Biomedical Heterogeneous Data Integration and Rank Retrieval using Data Bridges 基于数据桥的生物医学异构数据集成与等级检索

Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval Pub Date : 2019-07-18 DOI: 10.1145/3331184.3331417

P. Deshpande

{"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":null,"pages":null},"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}

引用次数: 1