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

The semileptonic baryonic decay Ds+→ppˉe+νeD_s^+\to p\bar p e^+ \nu_e

The decay $D_s^+\to p \bar p e^+\nu_e$ with a proton-antiproton pair in the final state is unique in the sense that it is the only semileptonic baryonic decay which is physically allowed in the charmed meson sector. Its measurement will test our basic knowledge on semileptonic $D_s^+$ decays and the low-energy $p\bar p$ interactions. Taking into account the major intermediate state contributions from $\eta, \eta', f_0(980)$ and $X(1835)$, we find that its branching fraction is at the level of $10^{-9} \sim 10^{-8}$. The location and the nature of $X(1835)$ state are crucial for the precise determination of the branching fraction. We wish to trigger a new round of a careful study with the upcoming more data in BESIII as well as the future super tau-charm factory.Comment: final version, accepted for publication in Phys. Lett.

Branching fractions of semileptonic DD and DsD_s decays from the covariant light-front quark model

Based on the predictions of the relevant form factors from the covariant light-front quark model, we show the branching fractions for the $D (D_s) \to (P,\,S,\,V,\,A)\,\ell\nu_\ell$ ($\ell=e$ or $\mu$) decays, where $P$ denotes the pseudoscalar meson, $S$ the scalar meson with a mass above 1 GeV, $V$ the vector meson and $A$ the axial-vector one. Comparison with the available experimental results are made, and we find an excellent agreement. The predictions for other decay modes can be tested in a charm factory, e.g., the BESIII detector. The future measurements will definitely further enrich our knowledge on the hadronic transition form factor as well as the inner structure of the even-parity mesons ($S$ and $A$).Comment: Predictions on D-> K1(1270), K1(1400) l nu rates correcte

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

Beyond Classification: Latent User Interests Profiling from Visual Contents Analysis

User preference profiling is an important task in modern online social networks (OSN). With the proliferation of image-centric social platforms, such as Pinterest, visual contents have become one of the most informative data streams for understanding user preferences. Traditional approaches usually treat visual content analysis as a general classification problem where one or more labels are assigned to each image. Although such an approach simplifies the process of image analysis, it misses the rich context and visual cues that play an important role in people's perception of images. In this paper, we explore the possibilities of learning a user's latent visual preferences directly from image contents. We propose a distance metric learning method based on Deep Convolutional Neural Networks (CNN) to directly extract similarity information from visual contents and use the derived distance metric to mine individual users' fine-grained visual preferences. Through our preliminary experiments using data from 5,790 Pinterest users, we show that even for the images within the same category, each user possesses distinct and individually-identifiable visual preferences that are consistent over their lifetime. Our results underscore the untapped potential of finer-grained visual preference profiling in understanding users' preferences.Comment: 2015 IEEE 15th International Conference on Data Mining Workshop