Fuzhen Zhuang
Fuzhen Zhuang

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zhuangfzatics.ict.ac.cn

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Bibliometrics: publication history
Average citations per article3.91
Citation Count184
Publication count47
Publication years2008-2017
Available for download11
Average downloads per article291.09
Downloads (cumulative)3,202
Downloads (12 Months)1,003
Downloads (6 Weeks)125
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49 results found Export Results: bibtexendnoteacmrefcsv

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1 published by ACM
November 2017 CIKM '17: Proceedings of the 2017 ACM on Conference on Information and Knowledge Management
Publisher: ACM
Bibliometrics:
Citation Count: 0
Downloads (6 Weeks): 9,   Downloads (12 Months): 25,   Downloads (Overall): 25

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Recently, Transfer Collaborative Filtering (TCF) methods across multiple source domains, which employ knowledge from different source domains to improve the recommendation performance in the target domain, have been applied in recommender systems. The existing multi-source TCF methods either require overlapping objects in different domains or simply re-weight domains to merge ...
Keywords: transfer collaborative filtering, local ensemble, recommender system

2 published by ACM
October 2017 ACM Transactions on Intelligent Systems and Technology (TIST) - Regular Papers: Volume 9 Issue 2, January 2018
Publisher: ACM
Bibliometrics:
Citation Count: 0
Downloads (6 Weeks): 18,   Downloads (12 Months): 60,   Downloads (Overall): 60

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Transfer learning has gained a lot of attention and interest in the past decade. One crucial research issue in transfer learning is how to find a good representation for instances of different domains such that the divergence between domains can be reduced with the new representation. Recently, deep learning has ...
Keywords: Double encoding-layer autoencoder, distribution difference measure, representation learning

3
August 2017 IJCAI'17: Proceedings of the 26th International Joint Conference on Artificial Intelligence
Publisher: AAAI Press
Bibliometrics:
Citation Count: 0

Existing multi-view learning methods based on kernel function either require the user to select and tune a single predefined kernel or have to compute and store many Gram matrices to perform multiple kernel learning. Apart from the huge consumption of manpower, computation and memory resources, most of these models seek ...

4
April 2017 WWW '17 Companion: Proceedings of the 26th International Conference on World Wide Web Companion
Publisher: International World Wide Web Conferences Steering Committee
Bibliometrics:
Citation Count: 0
Downloads (6 Weeks): 19,   Downloads (12 Months): 115,   Downloads (Overall): 115

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Recent studies in psychology suggest that novelty-seeking trait is highly related to consumer behavior, which has a profound business impact on online recommendation. This paper studies the problem of mining novelty seeking trait across domains to improve the recommendation performance in target domain. We propose an efficient model, CDNST, which ...
Keywords: recommendation novelty-seeking trait transfer learning

5 published by ACM
February 2017 WSDM '17: Proceedings of the Tenth ACM International Conference on Web Search and Data Mining
Publisher: ACM
Bibliometrics:
Citation Count: 0
Downloads (6 Weeks): 40,   Downloads (12 Months): 363,   Downloads (Overall): 363

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Last decades have witnessed a vast amount of interest and research in recommendation systems. Collaborative filtering, which uses the known preferences of a group of users to make recommendations or predictions of the unknown preferences for other users, is one of the most successful approaches to build recommendation systems. Most ...
Keywords: pair-wise constraints, representation learning, autoencoder, collaborative ranking

6
December 2016 Knowledge and Information Systems: Volume 49 Issue 3, December 2016
Publisher: Springer-Verlag New York, Inc.
Bibliometrics:
Citation Count: 2

Recently, there is a surge of social recommendation, which leverages social relations among users to improve recommendation performance. However, in many applications, social relations are very sparse or absent. Meanwhile, the attribute information of users or items may be rich. It is a big challenge to exploit this attribute information ...
Keywords: Heterogeneous information network, Matrix factorization, Recommender system, Similarity measure

7 published by ACM
October 2016 CIKM '16: Proceedings of the 25th ACM International on Conference on Information and Knowledge Management
Publisher: ACM
Bibliometrics:
Citation Count: 0
Downloads (6 Weeks): 4,   Downloads (12 Months): 79,   Downloads (Overall): 114

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In the past decade, there have been a large number of transfer learning algorithms proposed for various real-world applications. However, most of them are vulnerable to negative transfer since their performance is even worse than traditional supervised models. Aiming at more robust transfer learning models, we propose an ENsemble framework ...
Keywords: classifcation, transfer learning

8
September 2016 ECML PKDD 2016: European Conference on Machine Learning and Knowledge Discovery in Databases - Volume 9851
Publisher: Springer-Verlag New York, Inc.
Bibliometrics:
Citation Count: 0

In real world machine learning applications, testing data may contain some meaningful new categories that have not been seen in labeled training data. To simultaneously recognize new data categories and assign most appropriate category labels to the data actually from known categories, existing models assume the number of unknown new ...

9
July 2016 IJCAI'16: Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence
Publisher: AAAI Press
Bibliometrics:
Citation Count: 0

Last decades have witnessed a number of studies devoted to multi-view learning algorithms, however, few efforts have been made to handle online multi-view learning scenarios. In this paper, we propose an online Bayesian multi-view learning algorithm to learn predictive subspace with max-margin principle. Specifically, we first define the latent margin ...

10
April 2016 PAKDD 2016: Proceedings, Part II, of the 20th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining - Volume 9652
Publisher: Springer-Verlag New York, Inc.
Bibliometrics:
Citation Count: 0

Recently, social recommendation becomes a hot research direction, which leverages social relations among users to alleviate data sparsity and cold-start problems in recommender systems. The social recommendation methods usually employ simple similarity information of users as social regularization on users. Unfortunately, the widely used social regularization may suffer from several ...
Keywords: Heterogeneous information network, Regularization, Social recommendation

11
November 2015 Neurocomputing: Volume 168 Issue C, November 2015
Publisher: Elsevier Science Publishers B. V.
Bibliometrics:
Citation Count: 2

Modern cross-modal retrieving technology is required to find semantically relevant content from heterogeneous modalities. As previous studies construct unified dense correlation models on small scale cross-modal data, they are not capable of processing large scale Web data, because (a) the content of Web cross media is divergent; (b) the topic ...
Keywords: Cluster-sensitive, Structured correlation model, Correlation learning, Correspondence missing

12
November 2015 ICDM '15: Proceedings of the 2015 IEEE International Conference on Data Mining (ICDM)
Publisher: IEEE Computer Society
Bibliometrics:
Citation Count: 1

Multi-task learning aims at learning multiple related but different tasks. In general, there are two ways for multi-task learning. One is to exploit the small set of labeled data from all tasks to learn a shared feature space for knowledge sharing. In this way, the focus is on the labeled ...

13 published by ACM
October 2015 CIKM '15: Proceedings of the 24th ACM International on Conference on Information and Knowledge Management
Publisher: ACM
Bibliometrics:
Citation Count: 0
Downloads (6 Weeks): 4,   Downloads (12 Months): 35,   Downloads (Overall): 184

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This paper studies the novel learning scenarios of Distributed Online Multi-tasks (DOM), where the learning individuals with continuously arriving data are distributed separately and meanwhile they need to learn individual models collaboratively. It has three characteristics: distributed learning, online learning and multi-task learning. It is motivated by the emerging applications ...
Keywords: online learning, multi-task learning, distributed tasks

14 published by ACM
October 2015 CIKM '15: Proceedings of the 24th ACM International on Conference on Information and Knowledge Management
Publisher: ACM
Bibliometrics:
Citation Count: 2
Downloads (6 Weeks): 4,   Downloads (12 Months): 44,   Downloads (Overall): 165

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Multi-task Learning (MTL) aims to learn multiple related tasks simultaneously instead of separately to improve generalization performance of each task. Most existing MTL methods assumed that the multiple tasks to be learned have the same feature representation. However, this assumption may not hold for many real-world applications. In this paper, ...
Keywords: nonnegative matrix fatorization, heterogeneous features, multi-task learning

15
July 2015 IJCAI'15: Proceedings of the 24th International Conference on Artificial Intelligence
Publisher: AAAI Press
Bibliometrics:
Citation Count: 4

Transfer learning has attracted a lot of attention in the past decade. One crucial research issue in transfer learning is how to find a good representation for instances of different domains such that the divergence between domains can be reduced with the new representation. Recently, deep learning has been proposed ...

16
February 2015 Neurocomputing: Volume 149 Issue PA, February 2015
Publisher: Elsevier Science Publishers B. V.
Bibliometrics:
Citation Count: 9

Extreme learning machine (ELM) as an emerging technology has achieved exceptional performance in large-scale settings, and is well suited to binary and multi-class classification, as well as regression tasks. However, existing ELM and its variants predominantly employ single hidden layer feedforward networks, leaving the popular and potentially powerful stacked generalization ...
Keywords: DrELM, Stacked generalization, Deep learning, Extreme learning machine, Representation learning, Stacked ELMs

17
January 2015 AAAI'15: Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence
Publisher: AAAI Press
Bibliometrics:
Citation Count: 4

Supervised dimensionality reduction has shown great advantages in finding predictive subspaces. Previous methods rarely consider the popular maximum margin principle and are prone to overfitting to usually small training data, especially for those under the maximum likelihood framework. In this paper, we present a posterior-regularized Bayesian approach to combine Principal ...

18
January 2015 Fuzzy Sets and Systems: Volume 258 Issue C, January 2015
Publisher: Elsevier North-Holland, Inc.
Bibliometrics:
Citation Count: 2

Data are inherently uncertain in most applications. Uncertainty is encountered when an experiment such as sampling is to proceed, the result of which is not known to us while leading to variety of potential outcomes. With the rapid developments of data collection and distribution storage technologies, big data have become ...
Keywords: Uncertainty, Fuzzy boundary set, MapReduce, Sampling, Minimal consistent subset

19
December 2014 ICDM '14: Proceedings of the 2014 IEEE International Conference on Data Mining
Publisher: IEEE Computer Society
Bibliometrics:
Citation Count: 1

Most existing rat able aspect generating methods for aspect mining focus on identifying and rating aspects of reviews with overall ratings, while huge amount of unrated reviews are beyond their ability. This drawback motivates the research problem in this paper: predicting aspect ratings and overall ratings for unrated reviews. To ...
Keywords: Aspect Identification, Aspect Rating Prediction, Overall Rating Prediction

20 published by ACM
November 2014 CIKM '14: Proceedings of the 23rd ACM International Conference on Conference on Information and Knowledge Management
Publisher: ACM
Bibliometrics:
Citation Count: 7
Downloads (6 Weeks): 11,   Downloads (12 Months): 62,   Downloads (Overall): 295

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Multi-task multi-view learning deals with the learning scenarios where multiple tasks are associated with each other through multiple shared feature views. All previous works for this problem assume that the tasks use the same set of class labels. However, in real world there exist quite a few applications where the ...
Keywords: discriminant analysis, multi-class classification, multi-task learning, multi-view learning, heterogeneous tasks



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