Proceedings of the 25th ACM International on Conference on Information and Knowledge Management最新文献_第2页

FIN10K: A Web-based Information System for Financial Report Analysis and Visualization 基于web的财务报告分析和可视化信息系统

Proceedings of the 25th ACM International on Conference on Information and Knowledge Management Pub Date : 2016-10-24 DOI: 10.1145/2983323.2983328

Yu-Wen Liu, Liang‐Chih Liu, Chuan-Ju Wang, Ming-Feng Tsai

引用次数: 6

A Non-Parametric Topic Model for Short Texts Incorporating Word Coherence Knowledge 包含词连贯知识的短文本非参数主题模型

Proceedings of the 25th ACM International on Conference on Information and Knowledge Management Pub Date : 2016-10-24 DOI: 10.1145/2983323.2983898

Yuhao Zhang, W. Mao, D. Zeng

{"title":"A Non-Parametric Topic Model for Short Texts Incorporating Word Coherence Knowledge","authors":"Yuhao Zhang, W. Mao, D. Zeng","doi":"10.1145/2983323.2983898","DOIUrl":"https://doi.org/10.1145/2983323.2983898","url":null,"abstract":"Mining topics in short texts (e.g. tweets, instant messages) can help people grasp essential information and understand key contents, and is widely used in many applications related to social media and text analysis. The sparsity and noise of short texts often restrict the performance of traditional topic models like LDA. Recently proposed Biterm Topic Model (BTM) which models word co-occurrence patterns directly, is revealed effective for topic detection in short texts. However, BTM has two main drawbacks. It needs to manually specify topic number, which is difficult to accurately determine when facing new corpora. Besides, BTM assumes that two words in same term should belong to the same topic, which is often too strong as it does not differentiate two types of words (i.e. general words and topical words). To tackle these problems, in this paper, we propose a non-parametric topic model npCTM with the above distinction. Our model incorporates the Chinese restaurant process (CRP) into the BTM model to determine topic number automatically. Our model also distinguishes general words from topical words by jointly considering the distribution of these two word types for each word as well as word coherence information as prior knowledge. We carry out experimental studies on real-world twitter dataset. The results demonstrate the effectiveness of our method to discover coherent topics compared with the baseline methods.","PeriodicalId":250808,"journal":{"name":"Proceedings of the 25th ACM International on Conference on Information and Knowledge Management","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128792205","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

Who are My Familiar Strangers?: Revealing Hidden Friend Relations and Common Interests from Smart Card Data 谁是我熟悉的陌生人?:从智能卡数据中揭示隐藏的朋友关系和共同兴趣

Proceedings of the 25th ACM International on Conference on Information and Knowledge Management Pub Date : 2016-10-24 DOI: 10.1145/2983323.2983804

Fusang Zhang, Beihong Jin, Tingjian Ge, Qiang Ji, Yanling Cui

{"title":"Who are My Familiar Strangers?: Revealing Hidden Friend Relations and Common Interests from Smart Card Data","authors":"Fusang Zhang, Beihong Jin, Tingjian Ge, Qiang Ji, Yanling Cui","doi":"10.1145/2983323.2983804","DOIUrl":"https://doi.org/10.1145/2983323.2983804","url":null,"abstract":"The newly emerging location-based social networks (LBSN) such as Tinder and Momo extends social interaction from friends to strangers, providing novel experiences of making new friends. Familiar strangers refer to the strangers who meet frequently in daily life and may share common interests; thus they may be good candidates for friend recommendation. In this paper, we study the problem of discovering familiar strangers, specifically, public transportation trip companions, and their common interests. We collect 5.7 million transaction records of smart cards from about 3.02 million people in the city of Beijing, China. We first analyze this dataset and reveal the temporal and spatial characteristics of passenger encounter behaviors. Then we propose a stability metric to measure hidden friend relations. This metric facilitates us to employ community detection techniques to capture the communities of trip companions. Further, we infer common interests of each community using a topic model, i.e., LDA4HFC (Latent Dirichlet Allocation for Hidden Friend Communities) model. Such topics for communities help to understand how hidden friend clusters are formed. We evaluate our method using large-scale and real-world datasets, consisting of two-week smart card records and 901,855 Point of Interests (POIs) in Beijing. The results show that our method outperforms three baseline methods with higher recommendation accuracy. Moreover, our case study demonstrates that the discovered topics interpret the communities very well.","PeriodicalId":250808,"journal":{"name":"Proceedings of the 25th ACM International on Conference on Information and Knowledge Management","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126915598","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: 10

Efficient Orthogonal Non-negative Matrix Factorization over Stiefel Manifold Stiefel流形上的高效正交非负矩阵分解

Proceedings of the 25th ACM International on Conference on Information and Knowledge Management Pub Date : 2016-10-24 DOI: 10.1145/2983323.2983761

W. Zhang, Mingkui Tan, Quan Z. Sheng, Lina Yao, Javen Qinfeng Shi

引用次数: 9

Duer: Intelligent Personal Assistant Duer:智能个人助理

Proceedings of the 25th ACM International on Conference on Information and Knowledge Management Pub Date : 2016-10-24 DOI: 10.1145/2983323.2983372

Haifeng Wang

{"title":"Duer: Intelligent Personal Assistant","authors":"Haifeng Wang","doi":"10.1145/2983323.2983372","DOIUrl":"https://doi.org/10.1145/2983323.2983372","url":null,"abstract":"Intelligent personal assistant is widely recognized as a more natural and efficient way of human-computer interaction, which has attracted extensive interests from both academia and industry. In this talk, I describe Duer, Baidu's intelligent personal assistant. In particular, I would like to focus on the following three features. Firstly, Duer comprehensively understands people's requirements via multiple channels, including not only explicit utterances, but also user models and rich contexts. Duer's user models are learnt from users' interaction history, and the rich contexts consist of temporal and geographical information, as well as the foregoing dialogues. Secondly, Duer meets diverse requirements with a range of instruments, such as chatting, information provision, reminder service, etc. These instruments are implemented based on mining the big data of web pages, applications, and user logs, which are then seamlessly integrated in the dialogue flow. Thirdly, Duer features multi-modal interaction, which allows people to interact with it by means of texts, speech, and images. We believe the above features will enable Duer to become a better and distinguished intelligent assistant for each of you.","PeriodicalId":250808,"journal":{"name":"Proceedings of the 25th ACM International on Conference on Information and Knowledge Management","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130657075","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: 3

OrientStream: A Framework for Dynamic Resource Allocation in Distributed Data Stream Management Systems 面向分布式数据流管理系统的动态资源分配框架

Proceedings of the 25th ACM International on Conference on Information and Knowledge Management Pub Date : 2016-10-24 DOI: 10.1145/2983323.2983681

Chunkai Wang, Xiaofeng Meng, Qi Guo, Zujian Weng, Chen Yang

{"title":"OrientStream: A Framework for Dynamic Resource Allocation in Distributed Data Stream Management Systems","authors":"Chunkai Wang, Xiaofeng Meng, Qi Guo, Zujian Weng, Chen Yang","doi":"10.1145/2983323.2983681","DOIUrl":"https://doi.org/10.1145/2983323.2983681","url":null,"abstract":"Distributed data stream management systems (DDSMS) are usually composed of upper layer relational query systems (RQS) and lower layer stream processing systems (SPS). When users submit new queries to RQS, a query planner needs to be converted into a directed acyclic graph (DAG) consisting of tasks which are running on SPS. Based on different query requests and data stream properties, SPS need to configure different deployments strategies. However, how to dynamically predict deployment configurations of SPS to ensure the processing throughput and low resource usage is a great challenge. This article presents OrientStream, a framework for dynamic resource allocation in DDSMS using incremental machine learning techniques. By introducing the data-level, query plan-level, operator-level and cluster-level's four-level feature extraction mechanism, we firstly use the different query workloads as training sets to predict the resource usage of DDSMS and then select the optimal resource configuration from candidate settings based on the current query requests and stream properties. Finally, we validate our approach on the open source SPS--Storm. Experiments show that OrientStream can reduce CPU usage of 8%-15% and memory usage of 38%-48% respectively.","PeriodicalId":250808,"journal":{"name":"Proceedings of the 25th ACM International on Conference on Information and Knowledge Management","volume":"92 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116177356","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: 9

PIN-TRUST: Fast Trust Propagation Exploiting Positive, Implicit, and Negative Information PIN-TRUST:利用正面、隐含和负面信息的快速信任传播

Proceedings of the 25th ACM International on Conference on Information and Knowledge Management Pub Date : 2016-10-24 DOI: 10.1145/2983323.2983753

Min-Hee Jang, C. Faloutsos, Sang-Wook Kim, U. Kang, Jiwoon Ha

引用次数: 32

Discovering Temporal Purchase Patterns with Different Responses to Promotions 发现对促销有不同反应的时间购买模式

Proceedings of the 25th ACM International on Conference on Information and Knowledge Management Pub Date : 2016-10-24 DOI: 10.1145/2983323.2983665

Ling Luo, Bin Li, I. Koprinska, S. Berkovsky, Fang Chen

{"title":"Discovering Temporal Purchase Patterns with Different Responses to Promotions","authors":"Ling Luo, Bin Li, I. Koprinska, S. Berkovsky, Fang Chen","doi":"10.1145/2983323.2983665","DOIUrl":"https://doi.org/10.1145/2983323.2983665","url":null,"abstract":"The supermarkets often use sales promotions to attract customers and create brand loyalty. They would often like to know if their promotions are effective for various customers, so that better timing and more suitable rate can be planned in the future. Given a transaction data set collected by an Australian national supermarket chain, in this paper we conduct a case study aimed at discovering customers' long-term purchase patterns, which may be induced by preference changes, as well as short-term purchase patterns, which may be induced by promotions. Since purchase events of individual customers may be too sparse to model, we propose to discover a number of latent purchase patterns from the data. The latent purchase patterns are modeled via a mixture of non-homogeneous Poisson processes where each Poisson intensity function is composed by long-term and short-term components. Through the case study, 1) we validate that our model can accurately estimate the occurrences of purchase events; 2) we discover easy-to-interpret long-term gradual changes and short-term periodic changes in different customer groups; 3) we identify the customers who are receptive to promotions through the correlation between behavior patterns and the promotions, which is particularly worthwhile for target marketing.","PeriodicalId":250808,"journal":{"name":"Proceedings of the 25th ACM International on Conference on Information and Knowledge Management","volume":"95 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121493448","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: 14

Modeling Customer Engagement from Partial Observations 从部分观察中建模客户参与

Proceedings of the 25th ACM International on Conference on Information and Knowledge Management Pub Date : 2016-10-24 DOI: 10.1145/2983323.2983854

Jelena Stojanovic, Djordje Gligorijevic, Z. Obradovic

{"title":"Modeling Customer Engagement from Partial Observations","authors":"Jelena Stojanovic, Djordje Gligorijevic, Z. Obradovic","doi":"10.1145/2983323.2983854","DOIUrl":"https://doi.org/10.1145/2983323.2983854","url":null,"abstract":"It is of high interest for a company to identify customers expected to bring the largest profit in the upcoming period. Knowing as much as possible about each customer is crucial for such predictions. However, their demographic data, preferences, and other information that might be useful for building loyalty programs is often missing. Additionally, modeling relations among different customers as a network can be beneficial for predictions at an individual level, as similar customers tend to have similar purchasing patterns. We address this problem by proposing a robust framework for structured regression on deficient data in evolving networks with a supervised representation learning based on neural features embedding. The new method is compared to several unstructured and structured alternatives for predicting customer behavior (e.g. purchasing frequency and customer ticket) on user networks generated from customer databases of two companies from different industries. The obtained results show 4% to 130% improvement in accuracy over alternatives when all customer information is known. Additionally, the robustness of our method is demonstrated when up to 80% of demographic information was missing where it was up to several folds more accurate as compared to alternatives that are either ignoring cases with missing values or learn their feature representation in an unsupervised manner.","PeriodicalId":250808,"journal":{"name":"Proceedings of the 25th ACM International on Conference on Information and Knowledge Management","volume":"50 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114852324","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

Information Diffusion at Workplace 工作场所的资讯扩散

Proceedings of the 25th ACM International on Conference on Information and Knowledge Management Pub Date : 2016-10-24 DOI: 10.1145/2983323.2983848

Jiawei Zhang, Philip S. Yu, Yuanhua Lv, Qianyi Zhan

{"title":"Information Diffusion at Workplace","authors":"Jiawei Zhang, Philip S. Yu, Yuanhua Lv, Qianyi Zhan","doi":"10.1145/2983323.2983848","DOIUrl":"https://doi.org/10.1145/2983323.2983848","url":null,"abstract":"People nowadays need to spend a large amount of time on their work everyday and workplace has become an important social occasion for effective communication and information exchange among employees. Besides traditional online contacts (e.g., face-to-face meetings and telephone calls), to facilitate the communication and cooperation among employees, a new type of online social networks has been launched inside the firewalls of many companies, which are named as the \"enterprise social networks\" (ESNs). In this paper, we want to study the information diffusion among employees at workplace via both online ESNs and online contacts. This is formally defined as the IDE (Information Diffusion in Enterprise) problem. Several challenges need to be addressed in solving the IDE problem: (1) diffusion channel extraction from online ESN and online contacts; (2) effective aggregation of the information delivered via different diffusion channels; and (3) communication channel weighting and selection. A novel information diffusion model, Muse (Multi-source Multi-channel Multi-topic diffUsion SElection), is introduced in this paper to resolve these challenges. Extensive experiments conducted on real-world ESN and organizational chart dataset demonstrate the outstanding performance of Muse in addressing the IDE problem.","PeriodicalId":250808,"journal":{"name":"Proceedings of the 25th ACM International on Conference on Information and Knowledge Management","volume":"47 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124131569","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: 17