Proceedings of the 22nd ACM international conference on Information & Knowledge Management最新文献

Spatial search for K diverse-near neighbors K个异近邻的空间搜索

Proceedings of the 22nd ACM international conference on Information & Knowledge Management Pub Date : 2013-10-27 DOI: 10.1145/2505515.2505747

Gregory Ference, Wang-Chien Lee, Hui-Ju Hung, De-Nian Yang

{"title":"Spatial search for K diverse-near neighbors","authors":"Gregory Ference, Wang-Chien Lee, Hui-Ju Hung, De-Nian Yang","doi":"10.1145/2505515.2505747","DOIUrl":"https://doi.org/10.1145/2505515.2505747","url":null,"abstract":"To many location-based service applications that prefer diverse results, finding locations that are spatially diverse and close in proximity to a query point (e.g., the current location of a user) can be more useful than finding the k nearest neighbors/locations. In this paper, we investigate the problem of searching for the k Diverse-Near Neighbors (kDNNs)} in spatial space that is based upon the spatial diversity and proximity of candidate locations to the query point. While employing a conventional distance measure for proximity, we develop a new and intuitive diversity metric based upon the variance of the angles among the candidate locations with respect to the query point. Accordingly, we create a dynamic programming algorithm that finds the optimal kDNNs. Unfortunately, the dynamic programming algorithm, with a time complexity of O(kn3), incurs excessive computational cost. Therefore, we further propose two heuristic algorithms, namely, Distance-based Browsing (DistBrow) and Diversity-based Browsing (DivBrow) that provide high effectiveness while being efficient by exploring the search space prioritized upon the proximity to the query point and spatial diversity, respectively. Using real and synthetic datasets, we conduct a comprehensive performance evaluation. The results show that DistBrow and DivBrow have superior effectiveness compared to state-of-the-art algorithms while maintaining high efficiency.","PeriodicalId":20528,"journal":{"name":"Proceedings of the 22nd ACM international conference on Information & Knowledge Management","volume":"79 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2013-10-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73891715","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: 8

Entropy-based histograms for selectivity estimation 基于熵的直方图的选择性估计

Proceedings of the 22nd ACM international conference on Information & Knowledge Management Pub Date : 2013-10-27 DOI: 10.1145/2505515.2505756

Hien To, Kuorong Chiang, C. Shahabi

引用次数: 28

Exploring XML data is as easy as using maps 探索XML数据就像使用地图一样简单

Proceedings of the 22nd ACM international conference on Information & Knowledge Management Pub Date : 2013-10-27 DOI: 10.1145/2505515.2508201

Yong Zeng, Z. Bao, Guoliang Li, T. Ling

引用次数: 0

Identifying salient entities in web pages 识别网页中的显著实体

Proceedings of the 22nd ACM international conference on Information & Knowledge Management Pub Date : 2013-10-27 DOI: 10.1145/2505515.2505602

Michael Gamon, T. Yano, Xinying Song, Johnson Apacible, Patrick Pantel

引用次数: 34

QBEES: query by entity examples QBEES:按实体样例查询

Proceedings of the 22nd ACM international conference on Information & Knowledge Management Pub Date : 2013-10-27 DOI: 10.1145/2505515.2507873

S. Metzger, Ralf Schenkel, M. Sydow

引用次数: 26

Trustable aggregation of online ratings 可靠的在线评级汇总

Proceedings of the 22nd ACM international conference on Information & Knowledge Management Pub Date : 2013-10-27 DOI: 10.1145/2505515.2507863

Hyun-Kyo Oh, Sang-Wook Kim, Sunju Park, M. Zhou

引用次数: 5

Random walk-based graphical sampling in unbalanced heterogeneous bipartite social graphs 非平衡异构二部社会图中基于随机行走的图形抽样

Proceedings of the 22nd ACM international conference on Information & Knowledge Management Pub Date : 2013-10-27 DOI: 10.1145/2505515.2507822

Yusheng Xie, Zhengzhang Chen, Ankit Agrawal, A. Choudhary, Lu Liu

引用次数: 6

Nonparametric bayesian multitask collaborative filtering 非参数贝叶斯多任务协同过滤

Proceedings of the 22nd ACM international conference on Information & Knowledge Management Pub Date : 2013-10-27 DOI: 10.1145/2505515.2505517

S. Chatzis

{"title":"Nonparametric bayesian multitask collaborative filtering","authors":"S. Chatzis","doi":"10.1145/2505515.2505517","DOIUrl":"https://doi.org/10.1145/2505515.2505517","url":null,"abstract":"The dramatic rates new digital content becomes available has brought collaborative filtering systems to the epicenter of computer science research in the last decade. One of the greatest challenges collaborative filtering systems are confronted with is the data sparsity problem: users typically rate only very few items; thus, availability of historical data is not adequate to effectively perform prediction. To alleviate these issues, in this paper we propose a novel multitask collaborative filtering approach. Our approach is based on a coupled latent factor model of the users rating functions, which allows for coming up with an agile information sharing mechanism that extracts much richer task-correlation information compared to existing approaches. Formulation of our method is based on concepts from the field of Bayesian nonparametrics, specifically Indian Buffet Process priors, which allow for data-driven determination of the optimal number of underlying latent features (item characteristics and user traits) assumed in the context of the model. We experiment on several real-world datasets, demonstrating both the efficacy of our method, and its superiority over existing approaches.","PeriodicalId":20528,"journal":{"name":"Proceedings of the 22nd ACM international conference on Information & Knowledge Management","volume":"19 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2013-10-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80197138","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: 28

Domain-dependent/independent topic switching model for online reviews with numerical ratings 带有数字评级的在线评论的领域依赖/独立主题切换模型

Proceedings of the 22nd ACM international conference on Information & Knowledge Management Pub Date : 2013-10-27 DOI: 10.1145/2505515.2505540

Yasutoshi Ida, Takuma Nakamura, Takashi Matsumoto

{"title":"Domain-dependent/independent topic switching model for online reviews with numerical ratings","authors":"Yasutoshi Ida, Takuma Nakamura, Takashi Matsumoto","doi":"10.1145/2505515.2505540","DOIUrl":"https://doi.org/10.1145/2505515.2505540","url":null,"abstract":"We propose a domain-dependent/independent topic switching model based on Bayesian probabilistic modeling for modeling online product reviews that are accompanied with numerical ratings provided by users. In this model, each word is allocated to a domain-dependent topic or a domain-independent topic, and the distribution of topics in an online review is connected to an observed numerical rating via a linear regression model. Domain-dependent topics utilize domain information observed with a corpus, and domain-independent topics utilize the framework of Bayesian Nonparametrics, which can estimate the number of topics in posterior distributions. The posterior distribution is estimated via collapsed Gibbs sampling. Using real data, our proposed model had smaller mean square error and smaller average mean error with a small model size and achieved convergence in fewer iterations for a regression task involving online review ratings, outperforming a baseline model that did not consider domains. Moreover, the proposed model can also tell us whether the words are positive or negative in the form of continuous values. This feature allows us to extract domain-dependent and -independent sentiment words.","PeriodicalId":20528,"journal":{"name":"Proceedings of the 22nd ACM international conference on Information & Knowledge Management","volume":"29 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2013-10-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80391731","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

Automated snippet generation for online advertising 自动片段生成在线广告

Proceedings of the 22nd ACM international conference on Information & Knowledge Management Pub Date : 2013-10-27 DOI: 10.1145/2505515.2507876

Stamatina Thomaidou, Ismini Lourentzou, Panagiotis Katsivelis-Perakis, M. Vazirgiannis

引用次数: 25