Top-N Recommender System via Matrix Completion

Top-N recommender systems have been investigated widely both in industry and academia. However, the recommendation quality is far from satisfactory. In this paper, we propose a simple yet promising algorithm. We fill the user-item matrix based on a low-rank assumption and simultaneously keep the original information. To do that, a nonconvex rank relaxation rather than the nuclear norm is adopted to provide a better rank approximation and an efficient optimization strategy is designed. A comprehensive set of experiments on real datasets demonstrates that our method pushes the accuracy of Top-N recommendation to a new level.Comment: AAAI 201

Probing the baryogenesis and dark matter relaxed in phase transition by gravitational waves and colliders

The cosmological phase transition with Q-balls production mechanism can explain the baryogenesis and dark matter simultaneously, where constraints on dark matter masses and reverse dilution are significantly relaxed. We study how to probe this scenario by collider signals at QCD next-to-leading order and gravitational wave signals.Comment: 22 pages,9 figures,4 tables, published in Phys.Rev.

Twin Learning for Similarity and Clustering: A Unified Kernel Approach

Many similarity-based clustering methods work in two separate steps including similarity matrix computation and subsequent spectral clustering. However, similarity measurement is challenging because it is usually impacted by many factors, e.g., the choice of similarity metric, neighborhood size, scale of data, noise and outliers. Thus the learned similarity matrix is often not suitable, let alone optimal, for the subsequent clustering. In addition, nonlinear similarity often exists in many real world data which, however, has not been effectively considered by most existing methods. To tackle these two challenges, we propose a model to simultaneously learn cluster indicator matrix and similarity information in kernel spaces in a principled way. We show theoretical relationships to kernel k-means, k-means, and spectral clustering methods. Then, to address the practical issue of how to select the most suitable kernel for a particular clustering task, we further extend our model with a multiple kernel learning ability. With this joint model, we can automatically accomplish three subtasks of finding the best cluster indicator matrix, the most accurate similarity relations and the optimal combination of multiple kernels. By leveraging the interactions between these three subtasks in a joint framework, each subtask can be iteratively boosted by using the results of the others towards an overall optimal solution. Extensive experiments are performed to demonstrate the effectiveness of our method.Comment: Published in AAAI 201

Electroweak baryogenesis in the framework of the effective field theory

We study the electroweak baryogenesis in the framework of the effective field theory. Our study shows that by introducing a light singlet scalar particle and a dimension-5 operator, it can provide the strong first order phase transition and the source of the CP-violation during the phase transition, and then produce abundant particle phenomenology at zero temperature. We also show the constraints on the new physics scale from the observed baryon-to-photon ratio, the low-energy experiments, and the LHC data.Comment: 12 pages, 5 figures, 1 table; version published in Phys.Rev.