Matrix factorization with rating completion : an enhanced SVD Model for collaborative filtering recommender systems

Collaborative filtering algorithms, such as matrix factorization techniques, are recently gaining momentum due to their promising performance on recommender systems. However, most collaborative filtering algorithms suffer from data sparsity. Active learning algorithms are effective in reducing the sparsity problem for recommender systems by requesting users to give ratings to some items when they enter the systems. In this paper, a new matrix factorization model, called Enhanced SVD (ESVD) is proposed, which incorporates the classic matrix factorization algorithms with ratings completion inspired by active learning. In addition, the connection between the prediction accuracy and the density of matrix is built to further explore its potentials. We also propose the Multi-layer ESVD, which learns the model iteratively to further improve the prediction accuracy. To handle the imbalanced data sets that contain far more users than items or more items than users, the Item-wise ESVD and User-wise ESVD are presented, respectively. The proposed methods are evaluated on the famous Netflix and Movielens data sets. Experimental results validate their effectiveness in terms of both accuracy and efficiency when compared with traditional matrix factorization methods and active learning methods

Exploring the linear space of Feynman integrals via generating functions

Deriving a comprehensive set of reduction rules for Feynman integrals has been a longstanding challenge. In this paper, we present a proposed solution to this problem utilizing generating functions of Feynman integrals. By establishing and solving differential equations of these generating functions, we are able to derive a system of reduction rules that effectively reduce any associated Feynman integrals to their bases. We illustrate this method through various examples and observe its potential value in numerous scenarios.Comment: 11 pages, 4 figures, references adde

Industrial Relocation and CO2 Emission Intensity: Focus on the Potential Cross-Country Shift from China to India and SE Asia

The potential relocation of various industrial sectors from China to India and countries of the SE Asian region presents low cost opportunities for manufacturers, but also risks rising energy demand and CO2 emissions. A cross-country shift of industrial output would present challenges for controlling emissions since India and SE Asian countries present higher industrial emissions intensity than China. We find that although there is a convergence in emissions intensity in the Machinery manufacturing and Paper and Pulp industries, there are significant variations in all other industrial sectors. Indian emissions are double that of China in the Iron and Steel and Textile and Leather industries and almost triple in the cement industry; Indonesian emissions are almost double those of China in the Non-Metallic Minerals and Textile and Leather industries and 50% higher in the Chemical and Petrochemical industry. We demonstrate that the expected higher emissions are driven by both a higher fuel mix carbon intensity in the new countries and a higher energy intensity in their industrial activities. While industrial relocation could benefit certain countries financially, it would impose considerable threats to their energy supply security and capacity to comply with their Paris Agreement commitments