Stephen Chi-fai Chan

A collaborative filtering framework based on fuzzy association rules and multiple-level similarity

The rapid development of Internet technologies in recent decades has imposed a heavy information burden on users. This has led to the popularity of recommender systems, which provide advice to users about items they may like to examine. Collaborative Filtering (CF) is the most promising technique in recommender systems, providing personalized recommendations to users based on their previously expressed preferences and those of other similar users. This paper introduces a CF framework based on Fuzzy Association Rules and Multiple-level Similarity (FARAMS). FARAMS extended existing techniques by using fuzzy association rule mining, and takes advantage of product similarities in taxonomies to address data sparseness and nontransitive associations. Experimental results show that FARAMS improves prediction quality, as compared to similar approaches.

Modeling workflow processes with colored Petri nets

The definition and maintenance of workflow processes have become important tasks for enterprises as workflow management systems (WFMS) are systematically applied to critical business processes. In order to simplify the management and usage of workflow processes and to integrate with other applications, a good modeling method is essential. The WFCP-net (workflow-net based on colored Petri net) is an extension of the workflow-net (WF-net) which can be used to model family of workflow processes with similar process routes and logic rules. An expanding suite of tools, which currently includes, can support its application: a process structure graph editor, a WF script (workflow script language for writing business rules) editor and translator (translate WF script into Java classes) and a dynamically loadable workflow engine.

An empirical study of a cross-level association rule mining approach to cold-start recommendations

We propose a novel hybrid recommendation approach to address the well-known cold-start problem in Collaborative Filtering (CF). Our approach makes use of Cross-Level Association RulEs (CLARE) to integrate content information about domain items into collaborative filters. We first introduce a preference model comprising both user-item and item-item relationships in recommender systems, and present a motivating example of our work based on the model. We then describe how CLARE generates cold-start recommendations. We empirically evaluated the effectiveness of CLARE, which shows superior performance to related work in addressing the cold-start problem.

A probabilistic rating inference framework for mining user preferences from reviews

We propose a novel Probabilistic Rating infErence Framework, known as Pref, for mining user preferences from reviews and then mapping such preferences onto numerical rating scales. Pref applies existing linguistic processing techniques to extract opinion words and product features from reviews. It then estimates the sentimental orientations (SO) and strength of the opinion words using our proposed relative-frequency-based method. This method allows semantically similar words to have different SO, thereby addresses a major limitation of existing methods. Pref takes the intuitive relationships between class labels, which are scalar ratings, into consideration when assigning ratings to reviews. Empirical results validated the effectiveness of Pref against several related algorithms, and suggest that Pref can produce reasonably good results using a small training corpus. We also describe a useful application of Pref as a rating inference framework. Rating inference transforms user preferences described as natural language texts into numerical rating scales. This allows Collaborative Filtering (CF) algorithms, which operate mostly on databases of scalar ratings, to utilize textual reviews as an additional source of user preferences. We integrated Pref with a classical CF algorithm, and empirically demonstrated the advantages of using rating inference to augment ratings for CF.

Learning programming through fashion and design: a pilot summer course in wearable computing for middle school students

As enrollments in engineering and computer science programs around the world have fallen in recent years, those who wish to see this trend reversed take heart from findings that children are more likely to develop an abiding interest in technology if they are exposed to it at an early age [3, 9]. In line with this research, we now see more summer camps and workshops being offered to middle school students with the objective of teaching programming and computer technology [1, 6, 8, 12]. To offer students a stimulating and interesting environment while teaching computing subjects, the learning tools in these camps usually revolve around robots and graphical programming of animations or games. These tools tend to mainly attract youngsters who like robotics or game design. However, we believe that we can improve the diversity of the student pool by introducing other topics. In this paper, we describe our experience in designing and organizing a programming course that focuses on wearable computing, fashion and design for middle school students. We will show that 1) wearable computing is interesting and inspiring to the students, 2) wearable computing motivates both boys and girls to learn technology and computing, which implies that it may be able to increase the potential computer science population, 3) wearable computing can provide a space for students to exercise their creativity while at the same time, teaching them about technology and programming.

Collaborative solid modeling on the WWW

This project studied some of the issues associated with collaborative solid modeling (CSM) on the WWW and implemented some possible solutions. The CSM system developed oglers the following facilities for collaborative design: (I) an environment for multiple users to edit a shared solid object on the WWW synchronous!v, (2) representation of alternate versions of a solid by a single CSG tree, (3) support for dif/rerent modes of collaboration among the users. The system was written in JAVA and tested mainly in a UNIX environment.

On the use of hierarchical information in sequential mining-based XML document similarity computation

Measuring the structural similarity among XML documents is the task of finding their semantic correspondence and is fundamental to many web-based applications. While there exist several methods to address the problem, the data mining approach seems to be a novel, interesting and promising one. It explores the idea of extracting paths from XML documents, encoding them as sequences and finding the maximal frequent sequences using the sequential pattern mining algorithms. In view of the deficiencies encountered by ignoring the hierarchical information in encoding the paths for mining, a new sequential pattern mining scheme for XML document similarity computation is proposed in this paper. It makes use of a preorder tree representation (PTR) to encode the XML tree’s paths so that both the semantics of the elements and the hierarchical structure of the document can be taken into account when computing the structural similarity among documents. In addition, it proposes a postprocessing step to reuse the mined patterns to estimate the similarity of unmatched elements so that another metric to qualify the similarity between XML documents can be introduced. Encouraging experimental results were obtained and reported.

XML Document Clustering Using Common XPath

XML is becoming a common way of storing data. The elements and their arrangement in the document’s hierarchy not only describe the document structure but also imply the data’s semantic meaning, and hence provide valuable information to develop tools for manipulating XML documents. In this paper, we pursue a data mining approach to the problem of XML document clustering. We introduce a novel XML structural representation called common XPath (CXP), which encodes the frequently occurring elements with the hierarchical information, and propose to take the CXPs mined to form the feature vectors for XML document clustering. In other words, data mining acts as a feature extractor in the clustering process. Based on this idea, we devise a path-based XML document clustering algorithm called PBClustering which groups the documents according to their CXPs, i.e. their frequent structures. Encouraging simulation results are observed and reported.

i*CATch: a scalable plug-n-play wearable computing framework for novices and children

There has been much recent work in wearable computing that is directed at democratization of the field, to make it more accessible to the general public and more easily used by the hobbyist user. As the field becomes more diversified, there has also been a shift away from the highly specialized functionality of earlier applications towards aesthetics, creativity, design and self-expression, as well as a push towards using wearable computing as an outreach tool to broaden interest and exposure in engineering and computing. This paper presents the design and development of the i*CATch wearable computing framework, which was developed specifically for children and novices to the field. The i*CATch framework is based upon a bus-based architecture, and is more scalable than the current alternatives. It consists of a set of plug-and-play components, a construction platform with a standardized interface, and an easy-to-use hybrid text-graphical integrated development environment. We will also present results of the evaluation of the i*CATch framework in real teaching environments.

Applying Cross-Level Association Rule Mining to Cold-Start Recommendations

We propose a novel hybrid recommendation algorithm for addressing the well-known cold-start problem in Collaborative Filtering (CF). Our algorithm makes use of Cross- Level Association RulEs (CLARE) to integrate content information about domain items into collaborative filters. We first introduce a preference model comprising both user- item and item-item relationships in recommender systems, and then describe how the CLARE algorithm generates recommendations for cold-start items based on the preference model. Experimental results validated that CLARE is capable of recommending cold-start items, and that it increases the number of recommendable items significantly by addressing the cold-start problem.