Learning Tree-based Deep Model for Recommender Systems

Model-based methods for recommender systems have been studied extensively in recent years. In systems with large corpus, however, the calculation cost for the learnt model to predict all user-item preferences is tremendous, which makes full corpus retrieval extremely difficult. To overcome the calculation barriers, models such as matrix factorization resort to inner product form (i.e., model user-item preference as the inner product of user, item latent factors) and indexes to facilitate efficient approximate k-nearest neighbor searches. However, it still remains challenging to incorporate more expressive interaction forms between user and item features, e.g., interactions through deep neural networks, because of the calculation cost. In this paper, we focus on the problem of introducing arbitrary advanced models to recommender systems with large corpus. We propose a novel tree-based method which can provide logarithmic complexity w.r.t. corpus size even with more expressive models such as deep neural networks. Our main idea is to predict user interests from coarse to fine by traversing tree nodes in a top-down fashion and making decisions for each user-node pair. We also show that the tree structure can be jointly learnt towards better compatibility with users' interest distribution and hence facilitate both training and prediction. Experimental evaluations with two large-scale real-world datasets show that the proposed method significantly outperforms traditional methods. Online A/B test results in Taobao display advertising platform also demonstrate the effectiveness of the proposed method in production environments.Comment: Accepted by KDD 201

Learning Audio Sequence Representations for Acoustic Event Classification

Acoustic Event Classification (AEC) has become a significant task for machines to perceive the surrounding auditory scene. However, extracting effective representations that capture the underlying characteristics of the acoustic events is still challenging. Previous methods mainly focused on designing the audio features in a 'hand-crafted' manner. Interestingly, data-learnt features have been recently reported to show better performance. Up to now, these were only considered on the frame-level. In this paper, we propose an unsupervised learning framework to learn a vector representation of an audio sequence for AEC. This framework consists of a Recurrent Neural Network (RNN) encoder and a RNN decoder, which respectively transforms the variable-length audio sequence into a fixed-length vector and reconstructs the input sequence on the generated vector. After training the encoder-decoder, we feed the audio sequences to the encoder and then take the learnt vectors as the audio sequence representations. Compared with previous methods, the proposed method can not only deal with the problem of arbitrary-lengths of audio streams, but also learn the salient information of the sequence. Extensive evaluation on a large-size acoustic event database is performed, and the empirical results demonstrate that the learnt audio sequence representation yields a significant performance improvement by a large margin compared with other state-of-the-art hand-crafted sequence features for AEC

Low dose and fast grating-based x-ray phase-contrast imaging using the integrating-bucket phase modulation technique

X-ray phase-contrast imaging has experienced rapid development over the last few decades, and in this technology, the phase modulation strategy of phase-stepping is used most widely to measure the sample's phase signal. However, because of its discontinuous nature, phase-stepping has the defects of worse mechanical stability and high exposure dose, which greatly hinder its wide application in dynamic phase measurement and potential clinical applications. In this manuscript, we demonstrate preliminary research on the use of integrating-bucket phase modulation method to retrieve the phase information in grating-based X-ray phase-contrast imaging. Experimental results showed that our proposed method can be well employed to extract the differential phase-contrast image, compared with the current mostly used phase-stepping strategy, advantage of integrating-bucket phase modulation technique is that fast measurement and low dose are promising.Comment: 14 pages, 6 figure

The quadratic variation for mixed-fractional Brownian motion

Abstract Let W = λ B + ν B H ${W}=\lambda B+\nu B^{H}$ be a mixed-fractional Brownian motion with Hurst index 0 < H < 1 2 $0< H<\frac{1}{2}$ and λ , ν ≠ 0 $\lambda,\nu\neq0$ . In this paper we study the quadratic covariation [ f ( W ) , W ] ( H ) $[f({W}),{W}]^{(H)}$ defined by [ f ( W ) , W ] t ( H ) : = lim ε ↓ 0 1 ν 2 ε 2 H ∫ 0 t { f ( W s + ε ) − f ( W s ) } ( W s + ε − W s ) d η s $$\bigl[f({W}),{W}\bigr]^{(H)}_{t}:=\lim_{\varepsilon\downarrow 0} \frac{1}{\nu^{2}\varepsilon^{2H}} \int_{0}^{t} \bigl\{ f({W}_{ s+\varepsilon})-f({W}_{s}) \bigr\} ({W}_{s+\varepsilon}-{W}_{s}) \,d\eta_{s}$$ in probability, where f is a Borel function and η s = λ 2 s + ν 2 s 2 H $\eta_{s}=\lambda^{2}s+\nu^{2}s^{2H}$ . For some suitable function f we show that the quadratic covariation exists in L 2 ( Ω ) $L^{2}(\Omega)$ and the Itô formula F ( W t ) = F ( 0 ) + ∫ 0 t f ( W s ) d W s + 1 2 [ f ( W ) , W ] t ( H ) $$F({W}_{t})=F(0)+ \int_{0}^{t}f({W}_{s})\,dW_{s}+ \frac{1}{2}\bigl[f({W}),{W}\bigr]^{(H)}_{t}$$ holds for all absolutely continuous function F with F ′ = f $F'=f$ , where the integral is the Skorohod integral with respect to W

The association between XPC Lys939Gln gene polymorphism and urinary bladder cancer susceptibility: a systematic review and meta-analysis

BACKGROUND: Numerous epidemiological studies have been conducted to explore the association between the Lys939Gln polymorphism of Xeroderma pigmentosum group C (XPC) gene and urinary bladder cancer susceptibility. However, the results remain inconclusive. In order to derive a more precise estimation of this relationship, a large and update meta-analysis was performed in this study. METHODS: A comprehensive search was conducted through researching MEDLINE, EMBASE, PubMed, Web of Science, China Biomedical Literature database (CBM) and China National Knowledge Infrastructure (CNKI) databases before June 2013. Crude odds ratios (ORs) with 95% confidence intervals (CIs) were calculated to estimate the strength of the association. RESULTS: A total of 12 studies with 4828 cases and 4890 controls for evaluating the XPC Lys939Gln polymorphism and urinary bladder cancer were included. Overall, there was significant associations between the XPC Lys939Gln polymorphism and urinary bladder cancer risk were found for homozygous model (OR = 1.352, 95% CL = 1.088-1.681), heterozygous model (OR = 1.354, 95% CL = 1.085-1.688), and allele comparison (OR = 1.109, 95% CL = 1.013-1.214). In subgroup analysis by ethnicity and source of controls, there were still significant associations detected in some genetic models. CONCLUSION: Our meta-analysis suggested that the XPC Lys939Gln polymorphism contributed to the risk of urinary bladder cancer. VIRTUAL SLIDES: The virtual slide(s) for this article can be found here: