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An exploration of improving collaborative recommender systems via user-item subgroups 12

An exploration of improving collaborative recommender system...

引用

21st Annual Conference on World Wide Web, WWW'12

作者： Xu, Bin Bu, Jiajun Chen, Chun Cai, Deng Zhejiang Provincial Key Laboratory of Service Robot College of Computer Science Zhejiang University Hangzhou China State Key Lab. of CAD and CG College of Computer Science Zhejiang University Hangzhou China

ISBN: (纸本)9781450312295

Collaborative filtering (CF) is one of the most successful recommendation approaches. It typically associates a user with a group of like-minded users based on their preferences over all the items, and recommends to the user those items enjoyed by others in the group. However we find that two users with similar tastes on one item subset may have totally different tastes on another set. In other words, there exist many user-item subgroups each consisting of a subset of items and a group of like-minded users on these items. It is more natural to make preference predictions for a user via the correlated subgroups than the entire user-item matrix. In this paper, to find meaningful subgroups, we formulate the Multiclass Co-Clustering (MCoC) problem and propose an effective solution to it. Then we propose an unified framework to extend the traditional CF algorithms by utilizing the subgroups information for improving their top-N recommendation performance. Our approach can be seen as an extension of traditional clustering CF models. Systematic experiments on three real world data sets have demonstrated the effectiveness of our proposed approach.

关键词： Collaborative filtering

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Unsupervised face-name association via commute distance

Unsupervised face-name association via commute distance

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20th ACM International Conference on Multimedia, MM 2012

作者： Bu, Jiajun Xu, Bin Wu, Chenxia Chen, Chun Zhu, Jianke Cai, Deng He, Xiaofei Zhejiang Provincial Key Laboratory of Service Robot College of Computer Science Zhejiang University Hangzhou China State Key Lab. of CAD and CG College of Computer Science Zhejiang University Hangzhou China

ISBN: (纸本)9781450310895

Recently, the task of unsupervised face-name association has received a considerable interests in multimedia and information retrieval communities. It is quite different with the generic facial image annotation problem because of its unsupervised and ambiguous assignment properties. Specifically, the task of face-name association should obey the following three constraints: (1) a face can only be assigned to a name appearing in its associated caption or to null;(2) a name can be assigned to at most one face;and (3) a face can be assigned to at most one name. Many conventional methods have been proposed to tackle this task while suffering from some common problems, eg, many of them are computational expensive and hard to make the null assignment decision. In this paper, we design a novel framework named face-name association via commute distance (FACD), which judges face-name and face-null assignments under a unified framework via commute distance (CD) algorithm. Then, to further speed up the on-line processing, we propose a novel anchor-based commute distance (ACD) algorithm whose main idea is using the anchor point representation structure to accelerate the eigen-decomposition of the adjacency matrix of a graph. Systematic experiment results on a large scale and real world image-caption database with a total of 194,046 detected faces and 244,725 names show that our proposed approach outperforms many state-of-the-art methods in performance. Our framework is appropriate for a large scale and real-time system. © 2012 ACM.

关键词： Real time systems

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Document summarization based on data reconstruction 26

Document summarization based on data reconstruction

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26th AAAI Conference on Artificial Intelligence and the 24th Innovative Applications of Artificial Intelligence Conference, AAAI-12 / IAAI-12

作者： He, Zhanying Chen, Chun Bu, Jiajun Wang, Can Zhang, Lijun Cai, Deng He, Xiaofei Zhejiang Provincial Key Laboratory of Service Robot College of Computer Science Zhejiang University Hangzhou 310027 China State Key Lab. of CAD and CG College of Computer Science Zhejiang University Hangzhou 310058 China

ISBN: (纸本)9781577355687

Document summarization is of great value to many real world applications, such as snippets generation for search results and news headlines generation. Traditionally, document summarization is implemented by extracting sentences that cover the main topics of a document with a minimum redundancy. In this paper, we take a different perspective from data reconstruction and propose a novel framework named Document Summarization based on Data Reconstruction (DSDR). Specifically, our approach generates a summary which consist of those sentences that can best reconstruct the original document. To model the relationship among sentences, we introduce two objective functions: (1) linear reconstruction, which approximates the document by linear combinations of the selected sentences;(2) nonnegative linear reconstruction, which allows only additive, not subtractive, linear combinations. In this framework, the reconstruction error becomes a natural criterion for measuring the quality of the summary. For each objective function, we develop an efficient algorithm to solve the corresponding optimization problem. Extensive experiments on summarization benchmark data sets DUC 2006 and DUC 2007 demonstrate the effectiveness of our proposed approach. Copyright © 2012, Association for the Advancement of Artificial Intelligence. All rights reserved.

关键词： Data mining

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Efficient online learning for large-scale sparse Kernel logistic regression 26

Efficient online learning for large-scale sparse Kernel logi...

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26th AAAI Conference on Artificial Intelligence and the 24th Innovative Applications of Artificial Intelligence Conference, AAAI-12 / IAAI-12

作者： Zhang, Lijun Jin, Rong Chen, Chun Bu, Jiajun He, Xiaofei Zhejiang Provincial Key Laboratory of Service Robot College of Computer Science Zhejiang University Hangzhou 310027 China Department of Computer Science and Engineering Michigan State University East Lansing MI 48824 United States State Key Lab of CADandCG College of Computer Science Zhejiang University Hangzhou 310058 China

ISBN: (纸本)9781577355687

In this paper, we study the problem of large-scale Kernel Logistic Regression (KLR). A straightforward approach is to apply stochastic approximation to KLR. We refer to this approach as non-conservative online learning algorithm because it updates the kernel classifier after every received training example, leading to a dense classifier. To improve the sparsity of the KLR classifier, we propose two conservative online learning algorithms that update the classifier in a stochastic manner and generate sparse solutions. With appropriately designed updating strategies, our analysis shows that the two conservative algorithms enjoy similar theoretical guarantee as that of the non-conservative algorithm. Empirical studies on several benchmark data sets demonstrate that compared to batch-mode algorithms for KLR, the proposed conservative online learning algorithms are able to produce sparse KLR classifiers, and achieve similar classification accuracy but with significantly shorter training time. Furthermore, both the sparsity and classification accuracy of our methods are comparable to those of the online kernel SVM. Copyright © 2012, Association for the Advancement of Artificial Intelligence. All rights reserved.

关键词： Stochastic systems

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Discriminative codeword selection for image representation

Discriminative codeword selection for image representation

引用

18th ACM International Conference on Multimedia ACM Multimedia 2010, MM'10

作者： Zhang, Lijun Chen, Chun Bu, Jiajun Chen, Zhengguang Tan, Shulong He, Xiaofei Zhejiang Key Laboratory of Service Robot College of Computer Science Zhejiang University Hangzhou China State Key Lab. of CAD and CG College of Computer Science Zhejiang University Hangzhou China

ISBN: (纸本)9781605589336

Bag of features (BoF) representation has attracted an increasing amount of attention in large scale image processing systems. BoF representation treats images as loose collections of local invariant descriptors extracted from them. The visual codebook is generally constructed by using an unsupervised algorithm such as K-means to quantize the local descriptors into clusters. Images are then represented by the frequency histograms of the codewords contained in them. To build a compact and discriminative codebook, codeword selection has become an indispensable tool. However, most of the existing codeword selection algorithms are supervised and the human labeling may be very expensive. In this paper, we consider the problem of unsupervised codeword selection, and propose a novel algorithm called Discriminative Codeword Selection (DCS). Motivated from recent studies on discriminative clustering, the central idea of our proposed algorithm is to select those codewords so that the cluster structure of the image database can be best respected. Specifically, a multi-output linear function is fitted to model the relationship between the data matrix after codeword selection and the indicator matrix. The most discriminative codewords are thus defined as those leading to minimal fitting error. Experiments on image retrieval and clustering have demonstrated the effectiveness of the proposed method. © 2010 ACM.

关键词： Image retrieval

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DPLC: Dynamic packet length control in wireless sensor networks

DPLC: Dynamic packet length control in wireless sensor netwo...

引用

IEEE INFOCOM 2010

作者： Dong, Wei Liu, Xue Chen, Chun He, Yuan Chen, Gong Liu, Yunhao Bu, Jiajun Zhejiang Key Lab. of Service Robot College of Comp. Sci. Zhejiang University China School of Comp. Sci. McGill University Canada Dept. of Comp. Sci. and Eng. Hong Kong University of Science and Technology

ISBN: (纸本)9781424458363

Previous packet length optimizations for sensor networks often employ a fixed optimal length scheme, while in this study we present DPLC, a Dynamic Packet Length Control scheme. To make DPLC more efficient in terms of channel utilization, we incorporate a lightweight and accurate link estimation method that captures both physical channel conditions and interferences. We further provide two easy-to-use services, i.e., small message aggregation and large message fragmentation, to facilitate upper-layer application programming. The implementation of DPLC based on TinyOS 2.1 is lightweight, with respect to computation, memory, and header overhead. Our experiments using a real indoor testbed running CTP show that DPLC results in a 13% reduction in transmission overhead and a 41.8% reduction in energy consumption compared with the original protocol, and a 21% reduction in transmission overhead and a 15.1% reduction in energy consumption compared with simple aggregation schemes. ©2010 IEEE.

关键词： Energy utilization

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DPLC: Dynamic Packet Length Control in Wireless Sensor Networks

DPLC: Dynamic Packet Length Control in Wireless Sensor Netwo...

引用

2010 Proceedings IEEE INFOCOM

作者： Wei Dong Xue Liu Chun Chen Yuan He Gong Chen Yunhao Liu Jiajun Bu Zhejiang Key Lab. of Service Robot College of Comp. Sci. Zhejiang University School of Comp. Sci. McGill University Dept. of Comp. Sci. & Eng. Hong Kong University of Science and Technology

ISBN: (纸本)9781424458363

Previous packet length optimizations for sensor networks often employ a fixed optimal length scheme, while in this study we present DPLC, a Dynamic Packet Length Control scheme. To make DPLC more efficient in terms of channel utilization, we incorporate a lightweight and accurate link estimation method that captures both physical channel conditions and interferences. We further provide two easy-to-use services, i.e., small message aggregation and large message fragmentation, to facilitate upper-layer application programming. The implementation of DPLC based on TinyOS 2.1 is lightweight, with respect to computation, memory, and header overhead. Our experiments using a real indoor testbed running CTP show that DPLC results in a 13percent reduction in transmission overhead and a 41.8percent reduction in energy consumption compared with the original protocol, and a 21percent reduction in transmission overhead and a 15.1percent reduction in energy consumption compared with simple aggregation schemes.

关键词： Wireless sensor networks Sensor phenomena and characterization Interference Energy consumption Protocols Forward error correction Sensor systems Communications Society Communication system control Helium

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Convex experimental design using manifold structure for image retrieval 09

Convex experimental design using manifold structure for imag...

引用

17th ACM International Conference on Multimedia, MM'09, with Co-located Workshops and Symposiums

作者： Zhang, Lijun Chen, Chun Chen, Wei Bu, Jiajun Cai, Deng He, Xiaofei Zhejiang Key Laboratory of Service Robot College of Computer Science Zhejiang University Hangzhou China State Key Lab. of CAD and CG College of Computer Science Zhejiang University Hangzhou China

ISBN: (纸本)9781605586083

Content Based Image Retrieval (CBIR) has become one of the most active research areas in computer science. Relevance feedback is often used in CBIR systems to bridge the semantic gap. Typically, users are asked to make relevance judgements on some query results, and the feedback information is then used to re-rank the images in the database. An effective relevance feedback algorithm must provide the users with the most informative images with respect to the ranking function. In this paper, we propose a novel active learning algorithm, called Convex Laplacian Regularized Ioptimal Design (CLapRID), for relevance feedback image retrieval. Our algorithm is based on a regression model which minimizes the least square error on the labeled images and simultaneously preserves the intrinsic geometrical structure of the image space. It selects the most informative images which minimize the average predictive variance. The optimization problem of CLapRID can be cast as a semidefinite programming (SDP) problem, and solved via interior-point methods. Experimental results on COREL database have demonstrate the effectiveness of the proposed algorithm for relevance feedback image retrieval. Copyright 2009 ACM.

关键词： Convex optimization

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