Proceedings of the 27th ACM International Conference on Information and Knowledge Management最新文献

GraphRep: Boosting Text Mining, NLP and Information Retrieval with Graphs GraphRep:用图增强文本挖掘、自然语言处理和信息检索

Proceedings of the 27th ACM International Conference on Information and Knowledge Management Pub Date : 2018-10-17 DOI: 10.1145/3269206.3274273

M. Vazirgiannis, Fragkiskos D. Malliaros, Giannis Nikolentzos

引用次数: 13

Network-based Receivable Financing 网络化应收账款融资

Proceedings of the 27th ACM International Conference on Information and Knowledge Management Pub Date : 2018-10-17 DOI: 10.1145/3269206.3272017

Ilaria Bordino, Francesco Gullo

引用次数: 2

Optimizing Boiler Control in Real-Time with Machine Learning for Sustainability 实时优化锅炉控制与机器学习的可持续性

Proceedings of the 27th ACM International Conference on Information and Knowledge Management Pub Date : 2018-10-17 DOI: 10.1145/3269206.3272024

Yukun Ding, Jinglan Liu, Jinjun Xiong, Meng Jiang, Yiyu Shi

引用次数: 7

CEC

Proceedings of the 27th ACM International Conference on Information and Knowledge Management Pub Date : 2018-10-17 DOI: 10.1615/atoz.c.cec

Daniel Deutch, Nave Frost

引用次数: 4

ANR ANR

Proceedings of the 27th ACM International Conference on Information and Knowledge Management Pub Date : 2018-10-17 DOI: 10.1145/3269206.3271810

Jin Yao Chin, Kaiqi Zhao, Shafiq R. Joty, Gao Cong

引用次数: 10

CIKM 2018 Co-Located Workshops Summary CIKM 2018同地研讨会总结

Proceedings of the 27th ACM International Conference on Information and Knowledge Management Pub Date : 2018-10-17 DOI: 10.1145/3269206.3274267

A. Cuzzocrea, F. Bonchi, D. Gunopulos

引用次数: 4

Behavior-based Community Detection: Application to Host Assessment In Enterprise Information Networks 基于行为的社区检测:在企业信息网络主机评估中的应用

Proceedings of the 27th ACM International Conference on Information and Knowledge Management Pub Date : 2018-10-17 DOI: 10.1145/3269206.3272022

Cheng Cao, Zhengzhang Chen, James Caverlee, L. Tang, Chen Luo, Zhichun Li

{"title":"Behavior-based Community Detection: Application to Host Assessment In Enterprise Information Networks","authors":"Cheng Cao, Zhengzhang Chen, James Caverlee, L. Tang, Chen Luo, Zhichun Li","doi":"10.1145/3269206.3272022","DOIUrl":"https://doi.org/10.1145/3269206.3272022","url":null,"abstract":"Community detection in complex networks is a fundamental problem that attracts much attention across various disciplines. Previous studies have been mostly focusing on external connections between nodes (i.e., topology structure) in the network whereas largely ignoring internal intricacies (i.e., local behavior) of each node. A pair of nodes without any interaction can still share similar internal behaviors. For example, in an enterprise information network, compromised computers controlled by the same intruder often demonstrate similar abnormal behaviors even if they do not connect with each other. In this paper, we study the problem of community detection in enterprise information networks, where large-scale internal events and external events coexist on each host. The discovered host communities, capturing behavioral affinity, can benefit many comparative analysis tasks such as host anomaly assessment. In particular, we propose a novel community detection framework to identify behavior-based host communities in enterprise information networks, purely based on large-scale heterogeneous event data. We continue proposing an efficient method for assessing host's anomaly level by leveraging the detected host communities. Experimental results on enterprise networks demonstrate the effectiveness of our model.","PeriodicalId":331886,"journal":{"name":"Proceedings of the 27th ACM International Conference on Information and Knowledge Management","volume":"10 12","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114047198","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: 18

From Neural Re-Ranking to Neural Ranking: Learning a Sparse Representation for Inverted Indexing 从神经重排序到神经排序:学习倒排索引的稀疏表示

Proceedings of the 27th ACM International Conference on Information and Knowledge Management Pub Date : 2018-10-17 DOI: 10.1145/3269206.3271800

Hamed Zamani, Mostafa Dehghani, W. Bruce Croft, E. Learned-Miller, J. Kamps

{"title":"From Neural Re-Ranking to Neural Ranking: Learning a Sparse Representation for Inverted Indexing","authors":"Hamed Zamani, Mostafa Dehghani, W. Bruce Croft, E. Learned-Miller, J. Kamps","doi":"10.1145/3269206.3271800","DOIUrl":"https://doi.org/10.1145/3269206.3271800","url":null,"abstract":"The availability of massive data and computing power allowing for effective data driven neural approaches is having a major impact on machine learning and information retrieval research, but these models have a basic problem with efficiency. Current neural ranking models are implemented as multistage rankers: for efficiency reasons, the neural model only re-ranks the top ranked documents retrieved by a first-stage efficient ranker in response to a given query. Neural ranking models learn dense representations causing essentially every query term to match every document term, making it highly inefficient or intractable to rank the whole collection. The reliance on a first stage ranker creates a dual problem: First, the interaction and combination effects are not well understood. Second, the first stage ranker serves as a \"gate-keeper\" or filter, effectively blocking the potential of neural models to uncover new relevant documents. In this work, we propose a standalone neural ranking model (SNRM) by introducing a sparsity property to learn a latent sparse representation for each query and document. This representation captures the semantic relationship between the query and documents, but is also sparse enough to enable constructing an inverted index for the whole collection. We parameterize the sparsity of the model to yield a retrieval model as efficient as conventional term based models. Our model gains in efficiency without loss of effectiveness: it not only outperforms the existing term matching baselines, but also performs similarly to the recent re-ranking based neural models with dense representations. Our model can also take advantage of pseudo-relevance feedback for further improvements. More generally, our results demonstrate the importance of sparsity in neural IR models and show that dense representations can be pruned effectively, giving new insights about essential semantic features and their distributions.","PeriodicalId":331886,"journal":{"name":"Proceedings of the 27th ACM International Conference on Information and Knowledge Management","volume":"54 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114050628","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: 153

Point Symmetry-based Deep Clustering 基于点对称的深度聚类

Proceedings of the 27th ACM International Conference on Information and Knowledge Management Pub Date : 2018-10-17 DOI: 10.1145/3269206.3269328

Jose G. Moreno

引用次数: 1

Traffic-Cascade: Mining and Visualizing Lifecycles of Traffic Congestion Events Using Public Bus Trajectories 交通级联:利用公共汽车轨迹挖掘和可视化交通拥堵事件的生命周期

Proceedings of the 27th ACM International Conference on Information and Knowledge Management Pub Date : 2018-10-17 DOI: 10.1145/3269206.3269216

A. T. Kwee, Meng-Fen Chiang, Philips Kokoh Prasetyo, Ee-Peng Lim

{"title":"Traffic-Cascade: Mining and Visualizing Lifecycles of Traffic Congestion Events Using Public Bus Trajectories","authors":"A. T. Kwee, Meng-Fen Chiang, Philips Kokoh Prasetyo, Ee-Peng Lim","doi":"10.1145/3269206.3269216","DOIUrl":"https://doi.org/10.1145/3269206.3269216","url":null,"abstract":"As road transportation supports both economic and social activities in developed cities, it is important to maintain smooth traffic on all highways and local roads. Whenever possible, traffic congestions should be detected early and resolved quickly. While existing traffic monitoring dashboard systems have been put in place in many cities, these systems require high-cost vehicle speed monitoring instruments and detect traffic congestion as independent events. There is a lack of low-cost dashboards to inspect and analyze the lifecycle of traffic congestion which is critical in assessing the overall impact of congestion, determining the possible the source(s) of congestion and its evolution. In the absence of publicly available sophisticated road sensor data which measures on-road vehicle speed, we make use of publicly available vehicle trajectory data to detect the lifecycle of traffic congestion, also known as congestion cascade. We have developed Traffic-Cascade, a dashboard system to identify traffic congestion events, compile them into congestion cascades, and visualize them on a web dashboard. Traffic-Cascade unveils spatio-temporal insights of the congestion cascades.","PeriodicalId":331886,"journal":{"name":"Proceedings of the 27th ACM International Conference on Information and Knowledge Management","volume":"53 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128481293","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: 7