Towards user profiling for web recommendation

Collaborative recommendation is one of widely used recommendation systems, which recommend items to visitor on a basis of referring other's preference that is similar to current user. User profiling technique upon Web transaction data is able to capture such informative knowledge of user task or interest. With the discovered usage pattern information, it is likely to recommend Web users more preferred content or customize the Web presentation to visitors via collaborative recommendation. In addition, it is helpful to identify the underlying relationships among Web users, items as well as latent tasks during Web mining period. In this paper, we propose a Web recommendation framework based on user profiling technique. In this approach, we employ Probabilistic Latent Semantic Analysis (PLSA) to model the co-occurrence activities and develop a modified k-means clustering algorithm to build user profiles as the representatives of usage patterns. Moreover, the hidden task model is derived by characterizing the meaningful latent factor space. With the discovered user profiles, we then choose the most matched profile, which possesses the closely similar preference to current user and make collaborative recommendation based on the corresponding page weights appeared in the selected user profile. The preliminary experimental results performed on real world data sets show that the proposed approach is capable of making recommendation accurately and efficiently.

Increased availability and scalability for clustered services via the wait time calculation, trust based filtering and redirection of TCP connection requests

The paper describes two new transport layer (TCP) options and an expanded transport layer queuing strategy that facilitate three functions that are fundamental to the dispatching-based clustered service. A transport layer option has been developed to facilitate. the use of client wait time data within the service request processing of the cluster. A second transport layer option has been developed to facilitate the redirection of service requests by the cluster dispatcher to the cluster processing member. An expanded transport layer service request queuing strategy facilitates the trust based filtering of incoming service requests so that a graceful degradation of service delivery may be achieved during periods of overload - most dramatically evidenced by distributed denial of service attacks against the clustered service. We describe how these new options and queues have been implemented and successfully tested within the transport layer of the Linux kernel.

Multi-topic information filtering with a single user profile

In Information Filtering (IF) a user may be interested in several topics in parallel. But IF systems have been built on representational models derived from Information Retrieval and Text Categorization, which assume independence between terms. The linearity of these models results in user profiles that can only represent one topic of interest. We present a methodology that takes into account term dependencies to construct a single profile representation for multiple topics, in the form of a hierarchical term network. We also introduce a series of non-linear functions for evaluating documents against the profile. Initial experiments produced positive results.

WSRF-based modeling of clinical trial information for collaborative cancer research

The CancerGrid consortium is developing open-standards cancer informatics to address the challenges posed by modern cancer clinical trials. This paper presents the service-oriented software paradigm implemented in CancerGrid to derive clinical trial information management systems for collaborative cancer research across multiple institutions. Our proposal is founded on a combination of a clinical trial (meta)model and WSRF (Web Services Resource Framework), and is currently being evaluated for use in early phase trials. Although primarily targeted at cancer research, our approach is readily applicable to other areas for which a similar information model is available.

High frequency sensing interrogation system using fiber Bragg grating based microwave photonic filtering

A high frequency sensing interrogation system by using fiber Bragg grating based microwave photonic filtering is proposed, in which the wavelength measurement sensitivity is proportional to the RF modulation frequency applied to the optical signal.

High-frequency fiber Bragg grating sensing interrogation system using Sagnac-loop-based microwave photonic filtering

A novel high-frequency fiber Bragg grating (FBG) sensing interrogation system by using fiber Sagnac-loop-based microwave photonic filtering is proposed and experimentally demonstrated. By adopting the microwave photonic filtering, the wavelength shift of sensing FBG can be converted into amplitude variation of the modulated electronic radio-frequency (RF) signal. In the experiment, the strain applied onto the sensing FBG has been demodulated by measuring the intensity of the recovered RF signal, and by modulating the RF signal with different frequencies, different interrogation sensitivities can be achieved.

An architecture for a collaborative authoring system

Collaborative working with the aid of computers is increasing rapidly due to the widespread use of computer networks, geographic mobility of people, and small powerful personal computers. For the past ten years research has been conducted into this use of computing technology from a wide variety of perspectives and for a wide range of uses. This thesis adds to that previous work by examining the area of collaborative writing amongst groups of people. The research brings together a number of disciplines, namely sociology for examining group dynamics, psychology for understanding individual writing and learning processes, and computer science for database, networking, and programming theory. The project initially looks at groups and how they form, communicate, and work together, progressing on to look at writing and the cognitive processes it entails for both composition and retrieval. The thesis then details a set of issues which need to be addressed in a collaborative writing system. These issues are then followed by developing a model for collaborative writing, detailing an iterative process of co-ordination, writing and annotation, consolidation, and negotiation, based on a structured but extensible document model. Implementation issues for a collaborative application are then described, along with various methods of overcoming them. Finally the design and implementation of a collaborative writing system, named Collaborwriter, is described in detail, which concludes with some preliminary results from initial user trials and testing.

Knowledge-based systems and engineering software

The work described was carried out as part of a collaborative Alvey software engineering project (project number SE057). The project collaborators were the Inter-Disciplinary Higher Degrees Scheme of the University of Aston in Birmingham, BIS Applied Systems Ltd. (BIS) and the British Steel Corporation. The aim of the project was to investigate the potential application of knowledge-based systems (KBSs) to the design of commercial data processing (DP) systems. The work was primarily concerned with BIS's Structured Systems Design (SSD) methodology for DP systems development and how users of this methodology could be supported using KBS tools. The problems encountered by users of SSD are discussed and potential forms of computer-based support for inexpert designers are identified. The architecture for a support environment for SSD is proposed based on the integration of KBS and non-KBS tools for individual design tasks within SSD - The Intellipse system. The Intellipse system has two modes of operation - Advisor and Designer. The design, implementation and user-evaluation of Advisor are discussed. The results of a Designer feasibility study, the aim of which was to analyse major design tasks in SSD to assess their suitability for KBS support, are reported. The potential role of KBS tools in the domain of database design is discussed. The project involved extensive knowledge engineering sessions with expert DP systems designers. Some practical lessons in relation to KBS development are derived from this experience. The nature of the expertise possessed by expert designers is discussed. The need for operational KBSs to be built to the same standards as other commercial and industrial software is identified. A comparison between current KBS and conventional DP systems development is made. On the basis of this analysis, a structured development method for KBSs in proposed - the POLITE model. Some initial results of applying this method to KBS development are discussed. Several areas for further research and development are identified.

Projection and interpolation based techniques for structured and impulsive noise filtering

In this chapter we present the relevant mathematical background to address two well defined signal and image processing problems. Namely, the problem of structured noise filtering and the problem of interpolation of missing data. The former is addressed by recourse to oblique projection based techniques whilst the latter, which can be considered equivalent to impulsive noise filtering, is tackled by appropriate interpolation methods.

An information foraging theory based user study of an adaptive user interaction framework for content-based image retrieval

This paper presents the design and results of a task-based user study, based on Information Foraging Theory, on a novel user interaction framework - uInteract - for content-based image retrieval (CBIR). The framework includes a four-factor user interaction model and an interactive interface. The user study involves three focused evaluations, 12 simulated real life search tasks with different complexity levels, 12 comparative systems and 50 subjects. Information Foraging Theory is applied to the user study design and the quantitative data analysis. The systematic findings have not only shown how effective and easy to use the uInteract framework is, but also illustrate the value of Information Foraging Theory for interpreting user interaction with CBIR. © 2011 Springer-Verlag Berlin Heidelberg.

Applying Information Foraging Theory to understand user interaction with content-based image retrieval

The paper proposes an ISE (Information goal, Search strategy, Evaluation threshold) user classification model based on Information Foraging Theory for understanding user interaction with content-based image retrieval (CBIR). The proposed model is verified by a multiple linear regression analysis based on 50 users' interaction features collected from a task-based user study of interactive CBIR systems. To our best knowledge, this is the first principled user classification model in CBIR verified by a formal and systematic qualitative analysis of extensive user interaction data. Copyright 2010 ACM.

A four-factor user interaction model for content-based image retrieval

In order to bridge the “Semantic gap”, a number of relevance feedback (RF) mechanisms have been applied to content-based image retrieval (CBIR). However current RF techniques in most existing CBIR systems still lack satisfactory user interaction although some work has been done to improve the interaction as well as the search accuracy. In this paper, we propose a four-factor user interaction model and investigate its effects on CBIR by an empirical evaluation. Whilst the model was developed for our research purposes, we believe the model could be adapted to any content-based search system.

Enabling effective user interactions in content-based image retrieval

This paper presents an interactive content-based image retrieval framework—uInteract, for delivering a novel four-factor user interaction model visually. The four-factor user interaction model is an interactive relevance feedback mechanism that we proposed, aiming to improve the interaction between users and the CBIR system and in turn users overall search experience. In this paper, we present how the framework is developed to deliver the four-factor user interaction model, and how the visual interface is designed to support user interaction activities. From our preliminary user evaluation result on the ease of use and usefulness of the proposed framework, we have learnt what the users like about the framework and the aspects we could improve in future studies. Whilst the framework is developed for our research purposes, we believe the functionalities could be adapted to any content-based image search framework.

Immune-inspired adaptive information filtering

Adaptive information filtering is a challenging research problem. It requires the adaptation of a representation of a user’s multiple interests to various changes in them. We investigate the application of an immune-inspired approach to this problem. Nootropia, is a user profiling model that has many properties in common with computational models of the immune system that have been based on Franscisco Varela’s work. In this paper we concentrate on Nootropia’s evaluation. We define an evaluation methodology that uses virtual user’s to simulate various interest changes. The results show that Nootropia exhibits the desirable adaptive behaviour.

Nootropia : a user profiling model based on a self-organising term network

Artificial Immune Systems are well suited to the problem of using a profile representation of an individual’s or a group’s interests to evaluate documents. Nootropia is a user profiling model that exhibits similarities to models of the immune system that have been developed in the context of autopoietic theory. It uses a self-organising term network that can represent a user’s multiple interests and can adapt to both short-term variations and substantial changes in them. This allows Nootropia to drift, constantly following changes in the user’s multiple interests, and, thus, to become structurally coupled to the user.