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Identifying influential nodes in complex networks
User-Based Collaborative-Filtering Recommendation Algorithms on Hadoop
- Zhi-Dan Zhao, Mingsheng Shang
- Computer ScienceThird International Conference on Knowledge…
- 9 January 2010
This paper implements user-based CF algorithm on a cloud computing platform, namely Hadoop, to solve the scalability problem of CF.
Detecting overlapping communities of weighted networks via a local algorithm
Uncovering the information core in recommender systems
- Weishan Zeng, A. Zeng, Hao Liu, Mingsheng Shang, T. Zhou
- Computer ScienceScientific reports
- 25 February 2014
It is argued that in each online system there exists a group of core users who carry most of the information for recommendation, and this core user extraction method enables the recommender systems to achieve 90% of the accuracy of the top-L recommendation by taking only 20% ofThe users into account.
Collaborative filtering with diffusion-based similarity on tripartite graphs
Empirical analysis of web-based user-object bipartite networks
This letter reports the empirical analysis on two large-scale web sites, audioscrobbler.com and del.icio.us, and proposes a new index, named collaborative similarity, to quantify the diversity of tastes based on the collaborative selection.
A Highly Accurate Framework for Self-Labeled Semisupervised Classification in Industrial Applications
- Di Wu, Xin Luo, Guoyin Wang, Mingsheng Shang, Ye Yuan, Huyong Yan
- Computer ScienceIEEE Transactions on Industrial Informatics
- 1 March 2018
The main idea of this framework is to incorporate a differential-evolution-based positioning optimization algorithm for classification into the iterative self-labeling process, aiming at optimizing the positioning of newly labeled data.
Relevance is more significant than correlation: Information filtering on sparse data
This letter reports the opposite results that the latter method, making use of only the relevance information, can outperform the former method, especially when the data set is sparse.
On minimizing total energy consumption in the scheduling of virtual machine reservations
Efficient Extraction of Non-negative Latent Factors from High-Dimensional and Sparse Matrices in Industrial Applications
- Xin Luo, Mingsheng Shang, Shuai Li
- Computer ScienceIEEE 16th International Conference on Data Mining…
- 1 December 2016
Experimental results on five HiDS matrices generated by industrial applications indicate that INLF is able to acquire non-negative latent factors from them in a more efficient manner than any existing method does.