Proceedings of the ... ACM International Conference on Information & Knowledge Management. ACM International Conference on Information and Knowledge Management最新文献_第7页

Content based social behavior prediction: a multi-task learning approach 基于内容的社会行为预测:一种多任务学习方法

Proceedings of the ... ACM International Conference on Information & Knowledge Management. ACM International Conference on Information and Knowledge Management Pub Date : 2011-10-24 DOI: 10.1145/2063576.2063719

Hongliang Fei, R. Jiang, Yuhao Yang, Bo Luo, Jun Huan

{"title":"Content based social behavior prediction: a multi-task learning approach","authors":"Hongliang Fei, R. Jiang, Yuhao Yang, Bo Luo, Jun Huan","doi":"10.1145/2063576.2063719","DOIUrl":"https://doi.org/10.1145/2063576.2063719","url":null,"abstract":"Information Flow Studies analyze the principles and mechanisms of social information distribution and is an essential research topic in social networks. Traditional approaches are primarily based on the social network graph topology. However, topology itself can not accurately reflect the user interests or activities. In this paper, we adopt a \"microeconomics\" approach to study social information diffusion and aim to answer the question that how social information flow and socialization behaviors are related to content similarity and user interests. In particular, we study content-based social activity prediction, i.e., to predict a user's response (e.g. comment or like) to their friends' postings (e.g. blogs) w.r.t. message content. In our solution, we cast the social behavior prediction problem as a multi-task learning problem, in which each task corresponds to a user. We have designed a novel multi-task learning algorithm that is specifically designed for learning information flow in social networks. In our model, we apply l1 and Tikhonov regularization to obtain a sparse and smooth model in a linear multi-task learning framework. Using comprehensive experimental study, we have demonstrated the effectiveness of the proposed learning method.","PeriodicalId":74507,"journal":{"name":"Proceedings of the ... ACM International Conference on Information & Knowledge Management. ACM International Conference on Information and Knowledge Management","volume":"6 1","pages":"995-1000"},"PeriodicalIF":0.0,"publicationDate":"2011-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91165521","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: 23

Information diffusion in social networks: observing and affecting what society cares about 社会网络中的信息扩散:观察和影响社会所关注的事物

Proceedings of the ... ACM International Conference on Information & Knowledge Management. ACM International Conference on Information and Knowledge Management Pub Date : 2011-10-24 DOI: 10.1145/2063576.2064036

D. Agrawal, Ceren Budak, A. E. Abbadi

引用次数: 17

On bias problem in relevance feedback 关于相关反馈中的偏差问题

Proceedings of the ... ACM International Conference on Information & Knowledge Management. ACM International Conference on Information and Knowledge Management Pub Date : 2011-10-24 DOI: 10.1145/2063576.2063866

Qianli Xing, Yi Zhang, Lanbo Zhang

{"title":"On bias problem in relevance feedback","authors":"Qianli Xing, Yi Zhang, Lanbo Zhang","doi":"10.1145/2063576.2063866","DOIUrl":"https://doi.org/10.1145/2063576.2063866","url":null,"abstract":"Relevance feedback is an effective approach to improve retrieval quality over the initial query. Typical relevance feedback methods usually select top-ranked documents for relevance judgments, then query expansion or model updating are carried out based on the feedback documents. However, the number of feedback documents is usually limited due to expensive human labeling. Thus relevant documents in the feedback set are hardly representative of all relevant documents and the feedback set is actually biased. As a result, the performance of relevance feedback will get hurt. In this paper, we first show how and where the bias problem exists through experiments. Then we study how the bias can be reduced by utilizing the unlabeled documents. After analyzing the usefulness of a document to relevance feedback, we propose an approach that extends the feedback set with carefully selected unlabeled documents by heuristics. Our experiment results show that the extended feedback set has less bias than the original feedback set and better performance can be achieved when the extended feedback set is used for relevance feedback.","PeriodicalId":74507,"journal":{"name":"Proceedings of the ... ACM International Conference on Information & Knowledge Management. ACM International Conference on Information and Knowledge Management","volume":"15 1","pages":"1965-1968"},"PeriodicalIF":0.0,"publicationDate":"2011-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89579408","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

Exploiting longer cycles for link prediction in signed networks 在签名网络中利用更长的周期进行链路预测

Proceedings of the ... ACM International Conference on Information & Knowledge Management. ACM International Conference on Information and Knowledge Management Pub Date : 2011-10-24 DOI: 10.1145/2063576.2063742

Kai-Yang Chiang, Nagarajan Natarajan, Ambuj Tewari, I. Dhillon

{"title":"Exploiting longer cycles for link prediction in signed networks","authors":"Kai-Yang Chiang, Nagarajan Natarajan, Ambuj Tewari, I. Dhillon","doi":"10.1145/2063576.2063742","DOIUrl":"https://doi.org/10.1145/2063576.2063742","url":null,"abstract":"We consider the problem of link prediction in signed networks. Such networks arise on the web in a variety of ways when users can implicitly or explicitly tag their relationship with other users as positive or negative. The signed links thus created reflect social attitudes of the users towards each other in terms of friendship or trust. Our first contribution is to show how any quantitative measure of social imbalance in a network can be used to derive a link prediction algorithm. Our framework allows us to reinterpret some existing algorithms as well as derive new ones. Second, we extend the approach of Leskovec et al. (2010) by presenting a supervised machine learning based link prediction method that uses features derived from longer cycles in the network. The supervised method outperforms all previous approaches on 3 networks drawn from sources such as Epinions, Slashdot and Wikipedia. The supervised approach easily scales to these networks, the largest of which has 132k nodes and 841k edges. Most real-world networks have an overwhelmingly large proportion of positive edges and it is therefore easy to get a high overall accuracy at the cost of a high false positive rate. We see that our supervised method not only achieves good accuracy for sign prediction but is also especially effective in lowering the false positive rate.","PeriodicalId":74507,"journal":{"name":"Proceedings of the ... ACM International Conference on Information & Knowledge Management. ACM International Conference on Information and Knowledge Management","volume":"51 1","pages":"1157-1162"},"PeriodicalIF":0.0,"publicationDate":"2011-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89417575","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: 160

Learning kernels with upper bounds of leave-one-out error 学习误差上界为留一的核函数

Proceedings of the ... ACM International Conference on Information & Knowledge Management. ACM International Conference on Information and Knowledge Management Pub Date : 2011-10-24 DOI: 10.1145/2063576.2063927

Yong Liu, Shizhong Liao, Yuexian Hou

引用次数: 23

DTMBIO 2011: international workshop on data and textmining in biomedical informatics DTMBIO 2011:生物医学信息学中的数据和文本挖掘国际研讨会

Proceedings of the ... ACM International Conference on Information & Knowledge Management. ACM International Conference on Information and Knowledge Management Pub Date : 2011-10-24 DOI: 10.1145/2063576.2064041

S. Ananiadou, Doheon Lee, S. Navathe, Min-Keun Song

引用次数: 0

A framework for personalized and collaborative clustering of search results 搜索结果的个性化和协作聚类框架

Proceedings of the ... ACM International Conference on Information & Knowledge Management. ACM International Conference on Information and Knowledge Management Pub Date : 2011-10-24 DOI: 10.1145/2063576.2063662

D. Anastasiu, Byron J. Gao, David J. Buttler

{"title":"A framework for personalized and collaborative clustering of search results","authors":"D. Anastasiu, Byron J. Gao, David J. Buttler","doi":"10.1145/2063576.2063662","DOIUrl":"https://doi.org/10.1145/2063576.2063662","url":null,"abstract":"How to organize and present search results plays a critical role in the utility of search engines. Due to the unprecedented scale of the Web and diversity of search results, the common strategy of ranked lists has become increasingly inadequate, and clustering has been considered as a promising alternative. Clustering divides a long list of disparate search results into a few topic-coherent clusters, allowing the user to quickly locate relevant results by topic navigation. While many clustering algorithms have been proposed that innovate on the automatic clustering procedure, we introduce ClusteringWiki, the first prototype and framework for personalized clustering that allows direct user editing of the clustering results. Through a Wiki interface, the user can edit and annotate the membership, structure and labels of clusters for a personalized presentation. In addition, the edits and annotations can be shared among users as a mass-collaborative way of improving search result organization and search engine utility.","PeriodicalId":74507,"journal":{"name":"Proceedings of the ... ACM International Conference on Information & Knowledge Management. ACM International Conference on Information and Knowledge Management","volume":"12 1","pages":"573-582"},"PeriodicalIF":0.0,"publicationDate":"2011-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78505434","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: 16

Designing an ensemble classifier over subspace classifiers using iterative convergence routine 基于迭代收敛的子空间分类器集成分类器设计

Proceedings of the ... ACM International Conference on Information & Knowledge Management. ACM International Conference on Information and Knowledge Management Pub Date : 2011-10-24 DOI: 10.1145/2063576.2063678

B. Vinzamuri, K. Karlapalem

引用次数: 0

Utility-driven anonymization in data publishing 数据发布中公用事业驱动的匿名化

Proceedings of the ... ACM International Conference on Information & Knowledge Management. ACM International Conference on Information and Knowledge Management Pub Date : 2011-10-24 DOI: 10.1145/2063576.2063945

Mingqiang Xue, Panagiotis Karras, Chedy Raïssi, H. Pung

引用次数: 3

Discovering trending phrases on information streams 发现信息流上的流行短语

Proceedings of the ... ACM International Conference on Information & Knowledge Management. ACM International Conference on Information and Knowledge Management Pub Date : 2011-10-24 DOI: 10.1145/2063576.2063937

K. Kamath, James Caverlee

引用次数: 5