Author name cluster

Congfu Xu

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

10 papers

1 author row

IJCAI Conference 2017 Conference Paper

Tensor Completion with Side Information: A Riemannian Manifold Approach

Tengfei Zhou
Hui Qian
Zebang Shen
Chao Zhang
Congfu Xu

By restricting the iterate on a nonlinear manifold, the recently proposed Riemannian optimization methods prove to be both efficient and effective in low rank tensor completion problems. However, existing methods fail to exploit the easily accessible side information, due to their format mismatch. Consequently, there is still room for improvement. To fill the gap, in this paper, a novel Riemannian model is proposed to tightly integrate the original model and the side information by overcoming their inconsistency. For this model, an efficient Riemannian conjugate gradient descent solver is devised based on a new metric that captures the curvature of the objective. Numerical experiments suggest that our method is more accurate than the state-of-the-art without compromising the efficiency.

PDF Details

IJCAI Conference 2017 Conference Paper

Understanding Users' Budgets for Recommendation with Hierarchical Poisson Factorization

Yunhui Guo
Congfu Xu
Hanzhang Song
Xin Wang

People consume and rate products in online shopping websites. The historical purchases of customers reflect their personal consumption habits and indicate their future shopping behaviors. Traditional preference-based recommender systems try to provide recommendations by analyzing users' feedback such as ratings and clicks. But unfortunately, most of the existing recommendation algorithms ignore the budget of the users. So they cannot avoid recommending users with products that will exceed their budgets. And they also cannot understand how the users will assign their budgets to different products. In this paper, we develop a generative model named collaborative budget-aware Poisson factorization (CBPF) to connect users' ratings and budgets. The CBPF model is intuitive and highly interpretable. We compare the proposed model with several state-of-the-art budget-unaware recommendation methods on several real-world datasets. The results show the advantage of uncovering users' budgets for recommendation.

PDF Details

IJCAI Conference 2016 Conference Paper

Constrained Preference Embedding for Item Recommendation

Xin Wang
Congfu Xu
Yunhui Guo
Hui Qian

To learn users' preference, their feedback information is commonly modeled as scalars and integrated into matrix factorization (MF) based algorithms. Based on MF techniques, the preference degree is computed by the product of user and item vectors, which is also represented by scalars. On the contrary, in this paper, we express users' feedback as constrained vectors, and call the idea constrained preference embedding (CPE); it means that we regard users, items and all users' behavior as vectors. We find that this viewpoint is more flexible and powerful than traditional MF for item recommendation. For example, by the proposed assumption, users' heterogeneous actions can be coherently mined because all entities and actions can be transferred to a space of the same dimension. In addition, CPE is able to model the feedback of uncertain preference degree. To test our assumption, we propose two models called CPE-s and CPE-ps based on CPE for item recommendation, and show that the popular pair-wise ranking model BPR-MF can be deduced by some restrictions and variations on CPE-s. In the experiments, we will test CPE and the proposed algorithms, and prove their effectiveness.

PDF Details

AAAI Conference 2016 Conference Paper

Fast Hybrid Algorithm for Big Matrix Recovery

Tengfei Zhou
Hui Qian
Zebang Shen
Congfu Xu

Large-scale Nuclear Norm penalized Least Square problem (NNLS) is frequently encountered in estimation of low rank structures. In this paper we accelerate the solution procedure by combining non-smooth convex optimization with smooth Riemannian method. Our methods comprise of two phases. In the ﬁrst phase, we use Alternating Direction Method of Multipliers (ADMM) both to identify the ﬁx rank manifold where an optimum resides and to provide an initializer for the subsequent reﬁnement. In the second phase, two superlinearly convergent Riemannian methods: Riemannian NewTon (NT) and Riemannian Conjugate Gradient descent (CG) are adopted to improve the approximation over a ﬁx rank manifold. We prove that our Hybrid method of ADMM and NT (HADMNT) converges to an optimum of NNLS at least quadratically. The experiments on large-scale collaborative ﬁltering datasets demonstrate very competitive performance of these fast hybrid methods compared to the state-of-the-arts.

PDF Details

IJCAI Conference 2015 Conference Paper

Recommendation Algorithms for Optimizing Hit Rate, User Satisfaction and Website Revenue

Xin Wang
Yunhui Guo
Congfu Xu

We usually use hit rate to measure the performance of item recommendation algorithms. In addition to hit rate, we consider additional two important factors which are ignored by most previous works. First, we consider whether users are satisfied with the recommended items. It is possible that a user has bought an item but dislikes it. Hence high hit rate may not reflect high customer satisfaction. Second, we consider whether the website retailers are satisfied with the recommendation results. If a customer is interested in two products and wants to buy one of them, it may be better to suggest the item which can help bring more profit. Therefore, a good recommendation algorithm should not only consider improving hit rate but also consider optimizing user satisfaction and website revenue. In this paper, we propose two algorithms for the above purposes and design two modified hit rate based metrics to measure them. Experimental results on 10 real-world datasets show that our methods can not only achieve better hit rate, but also improve user satisfaction and website revenue comparing with the state-of-the-art models.

PDF Details

IS Journal 2014 Journal Article

An Adaptive Fusion Algorithm for Spam Detection

Congfu Xu
Baojun Su
Yunbiao Cheng
Weike Pan
Li Chen

Spam detection has become a critical component in various online systems such as email services, advertising engines, social media sites, and so on. Here, the authors use email services as an example, and present an adaptive fusion algorithm for spam detection (AFSD), which is a general, content-based approach and can be applied to nonemail spam detection tasks with little additional effort. The proposed algorithm uses n-grams of nontokenized text strings to represent an email, introduces a link function to convert the prediction scores of online learners to become more comparable, trains the online learners in a mistake-driven manner via thick thresholding to obtain highly competitive online learners, and designs update rules to adaptively integrate the online learners to capture different aspects of spams. The prediction performance of AFSD is studied on five public competition datasets and on one industry dataset, with the algorithm achieving significantly better results than several state-of-the-art approaches, including the champion solutions of the corresponding competitions.

Details DOI

AAAI Conference 2014 Conference Paper

Exact Subspace Clustering in Linear Time

Shusen Wang
Bojun Tu
Congfu Xu
Zhihua Zhang

Subspace clustering is an important unsupervised learning problem with wide applications in computer vision and data analysis. However, the state-of-the-art methods for this problem suffer from high time complexity—quadratic or cubic in n (the number of data instances). In this paper we exploit a data selection algorithm to speedup computation and the robust principal component analysis to strengthen robustness. Accordingly, we devise a scalable and robust subspace clustering method which costs time only linear in n. We prove theoretically that under certain mild assumptions our method solves the subspace clustering problem exactly even for grossly corrupted data. Our algorithm is based on very simple ideas, yet it is the only linear time algorithm with noiseless or noisy recovery guarantee. Finally, empirical results verify our theoretical analysis.

PDF Details

AAAI Conference 2011 Conference Paper

A Fast Spectral Relaxation Approach to Matrix Completion via Kronecker Products

Hui Zhao
Jiuqiang Han
Naiyan Wang
Congfu Xu
Zhihua Zhang

In the existing methods for solving matrix completion, such as singular value thresholding (SVT), soft-impute and ﬁxed point continuation (FPCA) algorithms, it is typically required to repeatedly implement singular value decompositions (SVD) of matrices. When the size of the matrix in question is large, the computational complexity of ﬁnding a solution is costly. To reduce this expensive computational complexity, we apply Kronecker products to handle the matrix completion problem. In particular, we propose using Kronecker factorization, which approximates a matrix by the Kronecker product of several matrices of smaller sizes. We introduce Kronecker factorization into the soft-impute framework and devise an effective matrix completion algorithm. Especially when the factorized matrices have about the same sizes, the computational complexity of our algorithm is improved substantially.

PDF Details

JMLR Journal 2010 Journal Article

Regularized Discriminant Analysis, Ridge Regression and Beyond

Zhihua Zhang
Guang Dai
Congfu Xu
Michael I. Jordan

Fisher linear discriminant analysis (FDA) and its kernel extension-kernel discriminant analysis (KDA)-are well known methods that consider dimensionality reduction and classification jointly. While widely deployed in practical problems, there are still unresolved issues surrounding their efficient implementation and their relationship with least mean squares procedures. In this paper we address these issues within the framework of regularized estimation. Our approach leads to a flexible and efficient implementation of FDA as well as KDA. We also uncover a general relationship between regularized discriminant analysis and ridge regression. This relationship yields variations on conventional FDA based on the pseudoinverse and a direct equivalence to an ordinary least squares estimator. [abs] [ pdf ][ bib ] &copy JMLR 2010. ( edit, beta )

PDF Details

IS Journal 2008 Journal Article

The Smart Architect: Scalable Ontology-Based Modeling of Ancient Chinese Architectures

Yong Liu
Congfu Xu
Qiong Zhang
Yunhe Pan

The Smart Architect is an innovative intelligent system that can generate ancient Chinese architectures of similar styles or structures automatically. Using an ontology-based approach to analyze different architectural styles, the system converts geometry primitives into semantic architecture components. The modeling process can be performed at semantic levels and requires only certain knowledge in the corresponding architectural domain. In addition, a granular-based knowledge-refining method obtains more accurate knowledge with respect to the specific domains.

Details DOI