2018 IEEE International Conference on Big Knowledge (ICBK)最新文献_第5页

The k-Means Forest Classifier for High Dimensional Data 高维数据的k-均值森林分类器

2018 IEEE International Conference on Big Knowledge (ICBK) Pub Date : 2018-11-01 DOI: 10.1109/ICBK.2018.00050

Zizhong Chen, Xin Ding, Shuyin Xia, Baiyun Chen

引用次数: 0

Recommending Long-Tail Items Using Extended Tripartite Graphs 利用扩展三部图推荐长尾项目

2018 IEEE International Conference on Big Knowledge (ICBK) Pub Date : 2018-11-01 DOI: 10.1109/ICBK.2018.00024

Andrew Luke, Joseph Johnson, Yiu-Kai Ng

{"title":"Recommending Long-Tail Items Using Extended Tripartite Graphs","authors":"Andrew Luke, Joseph Johnson, Yiu-Kai Ng","doi":"10.1109/ICBK.2018.00024","DOIUrl":"https://doi.org/10.1109/ICBK.2018.00024","url":null,"abstract":"With the popular and increasing power of the Internet these days, the effort of distributing and inventory costs of stocking various online retailing items are nearly negligible. In addition to selling popular, called \"short-head\", items in large quantities, online retailers, such as Amazon, offer a large number of unique items, called \"long tail\", with relatively small quantities sold. Retailers realize that it has high value to sell items from the long-tail category, since for users these long-tail items could meet the interest of them and surprise them simultaneously. Retailers also recognize that long-tail items can be an untapped source of revenue for a business; however, it is difficult to connect customers with long-tail items they are interested in, since they are unaware of them. Recommender systems help bridge the gap between users and long-tail items by learning user preferences and recommending appropriate items to them. In this paper, we propose a new tripartite graph recommender system, which is designed to suggest long-tail items. Compared with other graph-based recommender systems, our proposed recommendation system solves the tripartite variant problem suffered by existing approaches for having a low diversity score. A rework of the tripartite graph system is introduced, called the extended tripartite graph system, which enhances the performance of existing long-tail recommendation approaches measured by using two widely-used performance metrics: recall and diversity. Experimental results on the extended tripartite graph algorithm verify its merits and novelty.","PeriodicalId":144958,"journal":{"name":"2018 IEEE International Conference on Big Knowledge (ICBK)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126817433","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: 12

Recommendation with Generalized Logistic Transformation 基于广义Logistic变换的推荐

2018 IEEE International Conference on Big Knowledge (ICBK) Pub Date : 2018-11-01 DOI: 10.1109/ICBK.2018.00059

Zhuo-Lin Fu, Fan Min, Heng-Ru Zhang

引用次数: 0

Graph Embedding Based Query Construction Over Knowledge Graphs 基于知识图的图嵌入查询构造

2018 IEEE International Conference on Big Knowledge (ICBK) Pub Date : 2018-11-01 DOI: 10.1109/ICBK.2018.00009

Ruijie Wang, M. Wang, Jun Liu, Siyu Yao, Q. Zheng

{"title":"Graph Embedding Based Query Construction Over Knowledge Graphs","authors":"Ruijie Wang, M. Wang, Jun Liu, Siyu Yao, Q. Zheng","doi":"10.1109/ICBK.2018.00009","DOIUrl":"https://doi.org/10.1109/ICBK.2018.00009","url":null,"abstract":"Graph-structured queries provide an efficient way to retrieve the desired data from large-scale knowledge graphs. However, it is difficult for non-expert users to write such queries, and users prefer expressing their query intention through natural language questions. Therefore, automatically constructing graph-structured queries of given natural language questions has received wide attention in recent years. Most existing methods rely on natural language processing techniques to perform the query construction process, which is complicated and time-consuming. In this paper, we focus on the query construction process and propose a novel framework which stands on recent advances in knowledge graph embedding techniques. Our framework first encodes the underlying knowledge graph into a low-dimensional embedding space by leveraging the generalized local knowledge graphs. Then, given a natural language question, our framework computes the structure of the target query and determines the vertices/edges which form the target query based on the learned embedding vectors. Finally, the target graph-structured query is constructed according to the query structure and determined vertices/edges. Extensive experiments were conducted on the benchmark dataset. The results demonstrate that our framework outperforms several state-of-the-art baseline models regarding effectiveness and efficiency.","PeriodicalId":144958,"journal":{"name":"2018 IEEE International Conference on Big Knowledge (ICBK)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129265045","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: 5

Mixed-Copula VaR for Portfolio Risk Evaluation 组合风险评估的混合copula VaR

2018 IEEE International Conference on Big Knowledge (ICBK) Pub Date : 2018-11-01 DOI: 10.1109/ICBK.2018.00060

Lechuan Yin, Jiebin Chen, Zhao-Rong Lai

引用次数: 1

Snapshot Visualization of Complex Graphs with Force-Directed Algorithms 用力导向算法实现复杂图的快照可视化

2018 IEEE International Conference on Big Knowledge (ICBK) Pub Date : 2018-11-01 DOI: 10.1109/ICBK.2018.00026

Se-Hang Cheong, Yain-Whar Si

{"title":"Snapshot Visualization of Complex Graphs with Force-Directed Algorithms","authors":"Se-Hang Cheong, Yain-Whar Si","doi":"10.1109/ICBK.2018.00026","DOIUrl":"https://doi.org/10.1109/ICBK.2018.00026","url":null,"abstract":"Force-directed algorithms are widely used for visualizing graphs. However, these algorithms are computationally expensive in producing good quality layouts for complex graphs. The layout quality is largely influenced by execution time and methods' input parameters especially for large complex graphs. The snapshots of visualization generated from these algorithms are useful in presenting the current view or a past state of an information on timeslices. Therefore, researchers often need to make a trade-off between the quality of visualization and the selection of appropriate force-directed algorithms. In this paper, we evaluate the quality of snapshots generated from 7 force-directed algorithms in terms of number of edge crossing and the standard deviations of edge length. Our experimental results showed that KK, FA2 and DH algorithms cannot produce satisfactory visualizations for large graphs within the time limit. KK-MS-DS algorithm can process large and planar graphs but it does not perform well for graphs with low average degrees. KK-MS algorithm produces better visualizations for sparse and non-clustered graphs than KK-MS-DS algorithm.","PeriodicalId":144958,"journal":{"name":"2018 IEEE International Conference on Big Knowledge (ICBK)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133083068","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

Sentiment and Semantic Deep Hierarchical Attention Neural Network for Fine Grained News Classification 面向细粒度新闻分类的情感和语义深度层次注意神经网络

2018 IEEE International Conference on Big Knowledge (ICBK) Pub Date : 2018-11-01 DOI: 10.1109/ICBK.2018.00017

Sri Teja Allaparthi, Ganesh Yaparla, Vikram Pudi

引用次数: 1

Nonlinear Dimensionality Reduction with Judicial Document Learning 非线性降维与司法文书学习

2018 IEEE International Conference on Big Knowledge (ICBK) Pub Date : 2018-11-01 DOI: 10.1109/ICBK.2018.00066

Xiaofan Fang, Xianghao Zhao

引用次数: 2

Discovering IMRaD Structure with Different Classifiers 使用不同分类器发现IMRaD结构

2018 IEEE International Conference on Big Knowledge (ICBK) Pub Date : 2018-11-01 DOI: 10.1109/ICBK.2018.00034

Sergio Ribeiro, Jingtao Yao, D. Rezende

{"title":"Discovering IMRaD Structure with Different Classifiers","authors":"Sergio Ribeiro, Jingtao Yao, D. Rezende","doi":"10.1109/ICBK.2018.00034","DOIUrl":"https://doi.org/10.1109/ICBK.2018.00034","url":null,"abstract":"Information within published papers around the world in scientific journals are structured in the format of Introduction, Methodology, Results, and Conclusion (IMRaD). Human ability to read and analyze is not capable of processing these large amounts of information. If we could identify the structure and consequently extract it to a user who needs a part of the structure, particularly an article in a foreign language, time will be saved as result. Computational approaches like Machine Learning (ML) and Natural Language Processing (NLP) have been widely used for similar purposes. However, it is very important to identify which one, or which group of classifiers work better for a specific kind of problem. The objective of this work is to identify applicable classifiers by analyzing and comparing results produced by different ML classifiers used in locating and classifying sentences from abstract of a paper into the IMRaD structure. This work demonstrates the possibility of integrating ML and NLP for the articles' sentence classification based on the IMRaD structure. It also verifies that it is possible to achieve good results with simple implementations without the need of too many computational resources.","PeriodicalId":144958,"journal":{"name":"2018 IEEE International Conference on Big Knowledge (ICBK)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130991894","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

Robust Lifelong Multi-task Multi-view Representation Learning 鲁棒终身多任务多视图表示学习

2018 IEEE International Conference on Big Knowledge (ICBK) Pub Date : 2018-11-01 DOI: 10.1109/ICBK.2018.00020

Gan Sun, Yang Cong, Jun Li, Y. Fu

{"title":"Robust Lifelong Multi-task Multi-view Representation Learning","authors":"Gan Sun, Yang Cong, Jun Li, Y. Fu","doi":"10.1109/ICBK.2018.00020","DOIUrl":"https://doi.org/10.1109/ICBK.2018.00020","url":null,"abstract":"The state-of-the-art multi-task multi-view learning (MTMV) tackles the learning scenario where multiple tasks are associated with each other via multiple shared feature views. However, in online practical scenarios where the learning tasks have heterogeneous features collected from multiple views, e.g., multiple sources, the state-of-the-arts with single view cannot work well. To tackle this issue, in this paper, we propose a Robust Lifelong Multi-task Multi-view Representation Learning (rLM2L) model to accumulate the knowledge from online multi-view tasks. More specifically, we firstly design a set of view-specific libraries to maintain the intra-view correlation information of each view, and further impose an orthogonal promoting term to enforce libraries to be as independent as possible. When online new multi-view task is coming, rLM2L model decomposes all views of the new task into a common view-invariant space by transferring the knowledge of corresponding library. In this view-invariant space, capturing underlying inter-view correlation and identifying task-specific views for the new task are jointly employed via a robust multi-task learning formulation. Then the view-specific libraries can be refined over time to keep on improving across all tasks. For the model optimization, the proximal alternating linearized minimization algorithm is adopted to optimize our nonconvex model alternatively to achieve lifelong learning. Finally, extensive experiments on benchmark datasets shows that our proposed rLM2L model outperforms existing lifelong learning models, while it can discover task-specific views from sequential multi-view task with less computational burden.","PeriodicalId":144958,"journal":{"name":"2018 IEEE International Conference on Big Knowledge (ICBK)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124328062","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: 12