User profiling based on folksonomy information in Web 2.0 for personalized recommender systems

Information overload has become a serious issue for web users. Personalisation can provide effective solutions to overcome this problem. Recommender systems are one popular personalisation tool to help users deal with this issue. As the base of personalisation, the accuracy and efficiency of web user profiling affects the performances of recommender systems and other personalisation systems greatly. In Web 2.0, the emerging user information provides new possible solutions to profile users. Folksonomy or tag information is a kind of typical Web 2.0 information. Folksonomy implies the users‘ topic interests and opinion information. It becomes another source of important user information to profile users and to make recommendations. However, since tags are arbitrary words given by users, folksonomy contains a lot of noise such as tag synonyms, semantic ambiguities and personal tags. Such noise makes it difficult to profile users accurately or to make quality recommendations. This thesis investigates the distinctive features and multiple relationships of folksonomy and explores novel approaches to solve the tag quality problem and profile users accurately. Harvesting the wisdom of crowds and experts, three new user profiling approaches are proposed: folksonomy based user profiling approach, taxonomy based user profiling approach, hybrid user profiling approach based on folksonomy and taxonomy. The proposed user profiling approaches are applied to recommender systems to improve their performances. Based on the generated user profiles, the user and item based collaborative filtering approaches, combined with the content filtering methods, are proposed to make recommendations. The proposed new user profiling and recommendation approaches have been evaluated through extensive experiments. The effectiveness evaluation experiments were conducted on two real world datasets collected from Amazon.com and CiteULike websites. The experimental results demonstrate that the proposed user profiling and recommendation approaches outperform those related state-of-the-art approaches. In addition, this thesis proposes a parallel, scalable user profiling implementation approach based on advanced cloud computing techniques such as Hadoop, MapReduce and Cascading. The scalability evaluation experiments were conducted on a large scaled dataset collected from Del.icio.us website. This thesis contributes to effectively use the wisdom of crowds and expert to help users solve information overload issues through providing more accurate, effective and efficient user profiling and recommendation approaches. It also contributes to better usages of taxonomy information given by experts and folksonomy information contributed by users in Web 2.0.

Website customization : exploring a tag-based approach in the Australian banking context

Website customization can help to better fulfill the needs and wants of individual customers. It is an important aspect of customer satisfaction of online banking, especially among the younger generation. This dimension, however, is poorly addressed particularly in the Australian context. The proposed research aims to fulfill this gap by exploring the use of a popular Web 2.0 technology known as tags or user assigned metadata to facilitate customization at the interaction level. A prototype is proposed to demonstrate the various interaction-based customization types, evaluated through a series of experiments to assess the impact on customer satisfaction. The expected research outcome is a set of guidelines akin to interaction design patterns for aiding the design and implementation of the proposed tag-based approach.

A Personalized Ontology Model for Web Information Gathering

As a model for knowledge description and formalization, ontologies are widely used to represent user profiles in personalized web information gathering. However, when representing user profiles, many models have utilized only knowledge from either a global knowledge base or a user local information. In this paper, a personalized ontology model is proposed for knowledge representation and reasoning over user profiles. This model learns ontological user profiles from both a world knowledge base and user local instance repositories. The ontology model is evaluated by comparing it against benchmark models in web information gathering. The results show that this ontology model is successful.

Measuring transit service reliability using data from web-based transit user survey : a case study at Brisbane, Australia

This paper reports an empirical study on measuring transit service reliability using the data from a Web-based passenger survey on a major transit corridor in Brisbane, Australia. After an introduction of transit service reliability measures, the paper presents the results from the case study including study area, data collection, and reliability measures obtained. This includes data exploration of boarding/arrival lateness, in-vehicle time variation, waiting time variation, and headway adherence. Impacts of peak-period effects and separate operation on service reliability are examined. Relationships between transit service characteristics and passenger waiting time are also discussed. A summary of key findings and an agenda of future research are offered in conclusions.

Archiving nature’s heartbeat using smart phones

The impact of urban development and climate change has created the impetus to monitor changes in the environment, particularly, the behaviour, habitat and movement of fauna species. The aim of this chapter is to present the design and development of a sensor network based on smart phones to automatically collect and analyse acoustic and visual data for environmental monitoring purposes. Due to the communication and sophisticated programming facilities offered by smart phones, software tools can be developed to allow data to be collected, partially processed and sent to a remote server over the network for storage and further processing. This sensor network which employs a client-server architecture has been deployed in three applications: monitoring a rare bird species near Brisbane Airport, study of koalas behaviour at St Bees Island, and detection of fruit flies. The users of this system include scientists (e.g. ecologists, ornithologists, computer scientists) and community groups participating in data collection or reporting on the environment (e.g. students, bird watchers). The chapter focuses on the following aspects of our research: issues involved in using smart phones as sensors; the overall framework for data acquisition, data quality control, data management and analysis; current and future applications of the smart phone-based sensor network, and our future research directions.

Defending web services against denial of service attacks using client puzzles

The interoperable and loosely-coupled web services architecture, while beneficial, can be resource-intensive, and is thus susceptible to denial of service (DoS) attacks in which an attacker can use a relatively insignificant amount of resources to exhaust the computational resources of a web service. We investigate the effectiveness of defending web services from DoS attacks using client puzzles, a cryptographic countermeasure which provides a form of gradual authentication by requiring the client to solve some computationally difficult problems before access is granted. In particular, we describe a mechanism for integrating a hash-based puzzle into existing web services frameworks and analyze the effectiveness of the countermeasure using a variety of scenarios on a network testbed. Client puzzles are an effective defence against flooding attacks. They can also mitigate certain types of semantic-based attacks, although they may not be the optimal solution.

Gap analysis of intrusion detection in smart grids

Given the recent emergence of the smart grid and smart grid related technologies, their security is a prime concern. Intrusion detection provides a second line of defense. However, conventional intrusion detection systems (IDSs) are unable to adequately address the unique requirements of the smart grid. This paper presents a gap analysis of contemporary IDSs from a smart grid perspective. This paper highlights the lack of adequate intrusion detection within the smart grid and discusses the limitations of current IDSs approaches. The gap analysis identifies current IDSs as being unsuited to smart grid application without significant changes to address smart grid specific requirements.

Optimum demand side response of smart grid with renewable energy source and electrical vehicles

The paper presents a demand side response scheme,which assists electricity consumers to proactively control own demands in such a way to deliberately avert congestion periods on the electrical network. The scheme allows shifting loads from peak to low demand periods in an attempt to flattening the national electricity requirement. The scheme can be concurrently used to accommodate the utilization of renewable energy sources,that might be available at user’s premises. In addition the scheme allows a full-capacity utilization of the available electrical infrastructure by organizing a wide-use of electric vehicles. The scheme is applicable in the Eastern and Southern States of Australia managed by the Australian Energy Market Operator. The results indicate the potential of the scheme to achieve energy savings and release capacity to accommodate renewable energy and electrical vehicle technologies.

GPFlow: an intuitive environment for web-based scientific workflow

Increasingly scientists are using collections of software tools in their research. These tools are typically used in concert, often necessitating laborious and error-prone manual data reformatting and transfer. We present an intuitive workflow environment to support scientists with their research. The workflow, GPFlow, wraps legacy tools, presenting a high level, interactive web-based front end to scientists. The workflow backend is realized by a commercial grade workflow engine (Windows Workflow Foundation). The workflow model is inspired by spreadsheets and is novel in its support for an intuitive method of interaction enabling experimentation as required by many scientists, e.g. bioinformaticians. We apply GPFlow to two bioinformatics experiments and demonstrate its flexibility and simplicity.

Online banking customization via tag-based interaction

In this paper, we describe ongoing work on online banking customization with a particular focus on interaction. The scope of the study is confined to the Australian banking context where the lack of customization is evident. This paper puts forward the notion of using tags to facilitate personalized interactions in online banking. We argue that tags can afford simple and intuitive interactions unique to every individual in both online and mobile environments. Firstly, through a review of related literature, we frame our work in the customization domain. Secondly, we define a range of taggable resources in online banking. Thirdly, we describe our preliminary prototype implementation with respect to interaction customization types. Lastly, we conclude with a discussion on future work.