CubeSVD: a novel approach to personalized Web search
As the competition of Web search market increases, there is a high demand for personalized Web search to conduct retrieval incorporating Web users' information needs. This paper focuses on utilizing clickthrough data to improve Web search. Since millions of searches are conducted everyday, a search engine accumulates a large volume of clickthrough data, which records who submits queries and which pages he/she clicks on. The clickthrough data is highly sparse and contains different types of objects (user, query and Web page), and the relationships among these objects are also very complicated. By performing analysis on these data, we attempt to discover Web users' interests and the patterns that users locate information. In this paper, a novel approach CubeSVD is proposed to improve Web search. The clickthrough data is represented by a 3-order tensor, on which we perform 3-mode analysis using the higher-order singular value decomposition technique to automatically capture the latent factors that govern the relations among these multi-type objects: users, queries and Web pages. A tensor reconstructed based on the CubeSVD analysis reflects both the observed interactions among these objects and the implicit associations among them. Therefore, Web search activities can be carried out based on CubeSVD analysis. Experimental evaluations using a real-world data set collected from an MSN search engine show that CubeSVD achieves encouraging search results in comparison with some standard methods.

title: Latent semantic analysis for multiple-type interrelated data objects
abstract: Co-occurrence data is quite common in many real applications. Latent Semantic Analysis (LSA) has been successfully used to identify semantic relations in such data. However, LSA can only handle a single co-occurrence relationship between two types of objects. In practical applications, there are many cases where multiple types of objects exist and any pair of these objects could have a pairwise co-occurrence relation. All these co-occurrence relations can be exploited to alleviate data sparseness or to represent objects more meaningfully. In this paper, we propose a novel algorithm, M-LSA, which conducts latent semantic analysis by incorporating all pairwise co-occurrences among multiple types of objects. Based on the mutual reinforcement principle, M-LSA identifies the most salient concepts among the co-occurrence data and represents all the objects in a unified semantic space. M-LSA is general and we show that several variants of LSA are special cases of our algorithm. Experiment results show that M-LSA outperforms LSA on multiple applications, including collaborative filtering, text clustering, and text categorization.

title: A comparison of implicit and explicit links for web page classification
abstract: It is well known that Web-page classification can be enhanced by using hyperlinks that provide linkages between Web pages. However, in the Web space, hyperlinks are usually sparse, noisy and thus in many situations can only provide limited help in classification. In this paper, we extend the concept of linkages from explicit hyperlinks to implicit links built between Web pages. By observing that people who search the Web with the same queries often click on different, but related documents together, we draw implicit links between Web pages that are clicked after the same queries. Those pages are implicitly linked. We provide an approach for automatically building the implicit links between Web pages using Web query logs, together with a thorough comparison between the uses of implicit and explicit links in Web page classification. Our experimental results on a large dataset confirm that the use of the implicit links is better than using explicit links in classification performance, with an increase of more than 10.5% in terms of the Macro-F1 measurement.

title: GE-CKO: A Method to Optimize Composite Kernels for Web Page Classification
abstract: Most of current researches on Web page classification focus on leveraging heterogeneous features such as plain text, hyperlinks and anchor texts in an effective and efficient way. Composite kernel method is one topic of interest among them. It first selects a bunch of initial kernels, each of which is determined separately by a certain type of features. Then a classifier is trained based on a linear combination of these kernels. In this paper, we propose an effective way to optimize the linear combination of kernels. We proved that this problem is equivalent to solving a generalized eigenvalue problem. And the weight vector of the kernels is the eigenvector associated with the largest eigen-value. A support vector machine (SVM) classifier is then trained based on this optimized combination of kernels. Our experiment on the WebKB dataset has shown the effectiveness of our proposed method.

title: Building bridges for web query classification
abstract: Web query classification (QC) aims to classify Web users' queries, which are often short and ambiguous, into a set of target categories. QC has many applications including page ranking in Web search, targeted advertisement in response to queries, and personalization. In this paper, we present a novel approach for QC that outperforms the winning solution of the ACM KDDCUP 2005 competition, whose objective is to classify 800,000 real user queries. In our approach, we first build a bridging classifier on an intermediate taxonomy in an offline mode. This classifier is then used in an online mode to map user queries to the target categories via the above intermediate taxonomy. A major innovation is that by leveraging the similarity distribution over the intermediate taxonomy, we do not need to retrain a new classifier for each new set of target categories, and therefore the bridging classifier needs to be trained only once. In addition, we introduce category selection as a new method for narrowing down the scope of the intermediate taxonomy based on which we classify the queries. Category selection can improve both efficiency and effectiveness of the online classification. By combining our algorithm with the winning solution of KDDCUP 2005, we made an improvement by 9.7% and 3.8% in terms of precision and F1 respectively compared with the best results of KDDCUP 2005.

title: Document Summarization Using Conditional Random Fields
abstract: Many methods, including supervised and unsupervised algorithms, have been developed for extractive document summarization. Most supervised methods consider the summarization task as a two-class classification problem and classify each sentence individually without leveraging the relationship among sentences. The unsupervised methods use heuristic rules to select the most informative sentences into a summary directly, which are hard to generalize. In this paper, we present a Conditional Random Fields (CRF) based framework to keep the merits of the above two kinds of approaches while avoiding their disadvantages. What is more, the proposed framework can take the outcomes of previous methods as features and seamlessly integrate them. The key idea of our approach is to treat the summarization task as a sequence labeling problem. In this view, each document is a sequence of sentences and the summarization procedure labels the sentences by 1 and 0. The label of a sentence depends on the assignment of labels of others. We compared our proposed approach with eight existing methods on an open benchmark data set. The results show that our approach can improve the performance by more than 7.1% and 12.1% over the best supervised baseline and unsupervised baseline respectively in terms of two popular metrics F1 and ROUGE-2. Detailed analysis of the improvement is presented as well.

title: Hiding in the Crowd: Privacy Preservation on Evolving Streams through Correlation Tracking
abstract: We address the problem of preserving privacy in streams, which has received surprisingly limited attention. For static data, a well-studied and widely used approach is based on random perturbation of the data values. However, streams pose additional challenges. First, analysis of the data has to be performed incrementally, using limited processing time and buffer space, making batch approaches unsuitable. Second, the characteristics of streams evolve over time. Consequently, approaches based on global analysis of the data are not adequate. We show that it is possible to efficiently and effectively track the correlation and autocorrelation structure of multivariate streams and leverage it to add noise which maximally preserves privacy, in the sense that it is very hard to remove. Our techniques achieve much better results than previous static, global approaches, while requiring limited processing time and memory. We provide both a mathematical analysis and experimental evaluation on real data to validate the correctness, efficiency, and effectiveness of our algorithms.

title: Drag-and-drop pasting
abstract: In this paper, we present a user-friendly system for seamless image composition, which we call drag-and-drop pasting. We observe that for Poisson image editing [Perez et al. 2003] to work well, the user must carefully draw a boundary on the source image to indicate the region of interest, such that salient structures in source and target images do not conflict with each other along the boundary. To make Poisson image editing more practical and easy to use, we propose a new objective function to compute an optimized boundary condition. A shortest closed-path algorithm is designed to search for the location of the boundary. Moreover, to faithfully preserve the object's fractional boundary, we construct a blended guidance field to incorporate the object's alpha matte. To use our system, the user needs only to simply outline a region of interest in the source image, and then drag and drop it onto the target image. Experimental results demonstrate the effectiveness of our "drag-and-drop pasting" system.

title: Automatic cascade training with perturbation bias
abstract: Face detection methods based on cascade architecture have demonstrated fast and robust performance. Cascade learning is aided by the modularity of the architecture in which nodes are chained together to form a cascade. In this paper we present two new cascade learning results which address the decoupled nature of the cascade learning task. First, we introduce a cascade indifference curve framework, which connects the learning objectives for a node to the overall cascade performance. We derive a new cost function for node learning, which yields fully-automatic stopping conditions and improved detection performance. Second, we introduce the concept of perturbation bias, which leverages the statistical differences between target and non-target classes in a detection problem to obtain improved performance and robustness. We derive necessary and sufficient conditions for the success of the method and present experimental results.

title: Z Approach to Semantic Web
abstract: The Semantic Web (SW) service is a web application using Semantic Web techniques which usually involve cooperation between several intelligent agents. The design of SW systems requires precise modelling techniques to capture ontology domain properties and application functionalities. We believe that Z as a specification technique can contribute to the Semantic Web-based system development in many ways. In this paper, we firstly conduct a case study of applying Z to the design of a SW service system (online talk discovery), and then present translation techniques and tools which can extract the SW ontology from the Z model automatically. Furthermore, we discuss how existing Z tools, i.e. Z/EVES, can be used to improve the quality of SW ontology design.

title: Algorithmic Design Using Object-Z for Twig XML Queries Evaluation
abstract: Web technologies based on XML, e.g. Semantic Web and Web Services, facilitate Web automation and universally accessible content. One of the key factors for the success of XML-based technologies is of finding an efficient query evaluation algorithm for XML-based data models. An XML twig query is a complex selection predicate on both structure and content of a labelled XML document. Several novel twig query evaluation algorithms have been proposed recently. However, these algorithms are difficult to understand and hence implement due to high complexity. In this work, we present an algorithmic design for XML queries evaluation system using Object-Z. An Object-Z specification is developed to give a concise and logical description of the XML data model and the twig queries. It makes the twig query evaluation straight-forward, and allows different evaluation algorithms to be constructed easily and independently.