A novel process of group-oriented question reduction for rule-based recommendation websites

Several websites utilise a rule-base recommendation system, which generates choices based on a series of questionnaires, for recommending products to users. This approach has a high risk of customer attrition and the bottleneck is the questionnaire set. If the questioning process is too long, complex or tedious; users are most likely to quit the questionnaire before a product is recommended to them. If the questioning process is short; the user intensions cannot be gathered. The commonly used feature selection methods do not provide a satisfactory solution. We propose a novel process combining clustering, decisions tree and association rule mining for a group-oriented question reduction process. The question set is reduced according to common properties that are shared by a specific group of users. When applied on a real-world website, the proposed combined method outperforms the methods where the reduction of question is done only by using association rule mining or only by observing distribution within the group.

Tensor-based item recommendation using probabilistic ranking in social tagging systems

A common problem with the use of tensor modeling in generating quality recommendations for large datasets is scalability. In this paper, we propose the Tensor-based Recommendation using Probabilistic Ranking method that generates the reconstructed tensor using block-striped parallel matrix multiplication and then probabilistically calculates the preferences of user to rank the recommended items. Empirical analysis on two real-world datasets shows that the proposed method is scalable for large tensor datasets and is able to outperform the benchmarking methods in terms of accuracy.

Two-way recommendation methods for social networks

In this work, we present the challenges associated with the two-way recommendation methods in social networks and the solutions. We discuss them from the perspective of community-type social networks such as online dating networks.

A recommendation system for predicting risks across multiple business process instances

This paper proposes a recommendation system that supports process participants in taking risk-informed decisions, with the goal of reducing risks that may arise during process execution. Risk reduction involves decreasing the likelihood and severity of a process fault from occurring. Given a business process exposed to risks, e.g. a financial process exposed to a risk of reputation loss, we enact this process and whenever a process participant needs to provide input to the process, e.g. by selecting the next task to execute or by filling out a form, we suggest to the participant the action to perform which minimizes the predicted process risk. Risks are predicted by traversing decision trees generated from the logs of past process executions, which consider process data, involved resources, task durations and other information elements like task frequencies. When applied in the context of multiple process instances running concurrently, a second technique is employed that uses integer linear programming to compute the optimal assignment of resources to tasks to be performed, in order to deal with the interplay between risks relative to different instances. The recommendation system has been implemented as a set of components on top of the YAWL BPM system and its effectiveness has been evaluated using a real-life scenario, in collaboration with risk analysts of a large insurance company. The results, based on a simulation of the real-life scenario and its comparison with the event data provided by the company, show that the process instances executed concurrently complete with significantly fewer faults and with lower fault severities, when the recommendations provided by our recommendation system are taken into account.

Refining user and item profiles based on multidimensional data for top-n item recommendation

In recommender systems based on multidimensional data, additional metadata provides algorithms with more information for better understanding the interaction between users and items. However, most of the profiling approaches in neighbourhood-based recommendation approaches for multidimensional data merely split or project the dimensional data and lack the consideration of latent interaction between the dimensions of the data. In this paper, we propose a novel user/item profiling approach for Collaborative Filtering (CF) item recommendation on multidimensional data. We further present incremental profiling method for updating the profiles. For item recommendation, we seek to delve into different types of relations in data to understand the interaction between users and items more fully, and propose three multidimensional CF recommendation approaches for top-N item recommendations based on the proposed user/item profiles. The proposed multidimensional CF approaches are capable of incorporating not only localized relations of user-user and/or item-item neighbourhoods but also latent interaction between all dimensions of the data. Experimental results show significant improvements in terms of recommendation accuracy.

Transferability of cupped oyster EST (Expressed Sequence Tag)-derived SNP (Single Nucleotide Polymorphism) markers to related crassostrea and ostrea species

Single nucleotide polymorphisms (SNPs) are widely acknowledged as the marker of choice for many genetic and genomic applications because they show co-dominant inheritance, are highly abundant across genomes and are suitable for high-throughput genotyping. Here we evaluated the applicability of SNP markers developed from Crassostrea gigas and C. virginica expressed sequence tags (ESTs) in closely related Crassostrea and Ostrea species. A total of 213 putative interspecific level SNPs were identified from re-sequencing data in six amplicons, yielding on average of one interspecific level SNP per seven bp. High polymorphism levels were observed and the high success rate of transferability show that genic EST-derived SNP markers provide an efficient method for rapid marker development and SNP discovery in closely related oyster species. The six EST-SNP markers identified here will provide useful molecular tools for addressing questions in molecular ecology and evolution studies including for stock analysis (pedigree monitoring) in related oyster taxa.

Improved estimation of size-transition matrices using tag-recapture data

We derive a new method for determining size-transition matrices (STMs) that eliminates probabilities of negative growth and accounts for individual variability. STMs are an important part of size-structured models, which are used in the stock assessment of aquatic species. The elements of STMs represent the probability of growth from one size class to another, given a time step. The growth increment over this time step can be modelled with a variety of methods, but when a population construct is assumed for the underlying growth model, the resulting STM may contain entries that predict negative growth. To solve this problem, we use a maximum likelihood method that incorporates individual variability in the asymptotic length, relative age at tagging, and measurement error to obtain von Bertalanffy growth model parameter estimates. The statistical moments for the future length given an individual's previous length measurement and time at liberty are then derived. We moment match the true conditional distributions with skewed-normal distributions and use these to accurately estimate the elements of the STMs. The method is investigated with simulated tag-recapture data and tag-recapture data gathered from the Australian eastern king prawn (Melicertus plebejus).

Estimating equations for parameters in stochastic growth models from tag-recapture data

James (1991, Biometrics 47, 1519-1530) constructed unbiased estimating functions for estimating the two parameters in the von Bertalanffy growth curve from tag-recapture data. This paper provides unbiased estimating functions for a class of growth models that incorporate stochastic components and explanatory variables. a simulation study using seasonal growth models indicates that the proposed method works well while the least-squares methods that are commonly used in the literature may produce substantially biased estimates. The proposed model and method are also applied to real data from tagged rack lobsters to assess the possible seasonal effect on growth.

Effective web service discovery using a combination of a semantic model and a data mining technique

With the advent of Service Oriented Architecture, Web Services have gained tremendous popularity. Due to the availability of a large number of Web services, finding an appropriate Web service according to the requirement of the user is a challenge. This warrants the need to establish an effective and reliable process of Web service discovery. A considerable body of research has emerged to develop methods to improve the accuracy of Web service discovery to match the best service. The process of Web service discovery results in suggesting many individual services that partially fulfil the user’s interest. By considering the semantic relationships of words used in describing the services as well as the use of input and output parameters can lead to accurate Web service discovery. Appropriate linking of individual matched services should fully satisfy the requirements which the user is looking for. This research proposes to integrate a semantic model and a data mining technique to enhance the accuracy of Web service discovery. A novel three-phase Web service discovery methodology has been proposed. The first phase performs match-making to find semantically similar Web services for a user query. In order to perform semantic analysis on the content present in the Web service description language document, the support-based latent semantic kernel is constructed using an innovative concept of binning and merging on the large quantity of text documents covering diverse areas of domain of knowledge. The use of a generic latent semantic kernel constructed with a large number of terms helps to find the hidden meaning of the query terms which otherwise could not be found. Sometimes a single Web service is unable to fully satisfy the requirement of the user. In such cases, a composition of multiple inter-related Web services is presented to the user. The task of checking the possibility of linking multiple Web services is done in the second phase. Once the feasibility of linking Web services is checked, the objective is to provide the user with the best composition of Web services. In the link analysis phase, the Web services are modelled as nodes of a graph and an allpair shortest-path algorithm is applied to find the optimum path at the minimum cost for traversal. The third phase which is the system integration, integrates the results from the preceding two phases by using an original fusion algorithm in the fusion engine. Finally, the recommendation engine which is an integral part of the system integration phase makes the final recommendations including individual and composite Web services to the user. In order to evaluate the performance of the proposed method, extensive experimentation has been performed. Results of the proposed support-based semantic kernel method of Web service discovery are compared with the results of the standard keyword-based information-retrieval method and a clustering-based machine-learning method of Web service discovery. The proposed method outperforms both information-retrieval and machine-learning based methods. Experimental results and statistical analysis also show that the best Web services compositions are obtained by considering 10 to 15 Web services that are found in phase-I for linking. Empirical results also ascertain that the fusion engine boosts the accuracy of Web service discovery by combining the inputs from both the semantic analysis (phase-I) and the link analysis (phase-II) in a systematic fashion. Overall, the accuracy of Web service discovery with the proposed method shows a significant improvement over traditional discovery methods.

Building procurement methods

A plethora of methods for procuring building projects are available to meet the needs of clients. Deciding what method to use for a given project is a difficult and challenging task as a client’s objectives and priorities need to marry with the selected method so as to improve the likelihood of the project being procured successfully. The decision as to what procurement system to use should be made as early as possible and underpinned by the client’s business case for the project. The risks and how they can potentially affect the client’s business should also be considered. In this report, the need for client’s to develop a procurement strategy, which outlines the key means by which the objectives of the project are to be achieved is emphasised. Once a client has established a business case for a project, appointed a principal advisor, determined their requirements and brief, then consideration as to which procurement method to be adopted should be made. An understanding of the characteristics of various procurement options is required before a recommendation can be made to a client. Procurement systems can be categorised as traditional, design and construct, management and collaborative. The characteristics of these systems along with the procurement methods commonly used are described. The main advantages and disadvantages, and circumstances under which a system could be considered applicable for a given project are also identified.

A framework for assessing social policy inclusion proposals

This document outlines a framework that could be used by government agencies in assessing policy interventions aimed at achieving social outcomes from government construction contracts. The framework represents a rational interpretation of the information gathered during the multi-outcomes construction policies project. The multi-outcomes project focused on the costs and benefits of using public construction contracts to promote the achievement of training and employment and public art objectives. The origin of the policy framework in a cost-benefit appraisal of current policy interventions is evidenced by its emphasis on sensitivity to policy commitment and project circumstances (especially project size and scope).The quantitative and qualitative analysis conducted in the multi-outcomes project highlighted, first, that in the absence of strong industry commitment to policy objectives, policy interventions typically result in high levels of avoidance activity, substantial administrative costs and very few benefits. Thus, for policy action on, for example, training or local employment to be successful compliance issues must be adequately addressed. Currently it appears that pre-qualification schemes (similar to the Priority Access Scheme) and schemes that rely on measuring, for example, the training investments of contractors within particular projects do not achieve high levels of compliance and involve significant administrative costs. Thus, an alternative is suggested in the policy framework developed here: a levy on each public construction project – set as a proportion of the total project costs. Although a full evaluation of this policy alternative was beyond the scope of the multi-outcomes construction policies project, it appears to offer the potential to minimize the transaction costs on contractors whilst enabling the creation of a training agency dedicated to improving the supply of skilled construction labour. A recommendation is thus made that this policy alternative be fully researched and evaluated. As noted above, the outcomes of the multi-outcomes research project also highlighted the need for sensitivity to project circumstances in the development and implementation of polices for public construction projects. Ideally a policy framework would have the flexibility to respond to circumstances where contractors share a commitment to the policy objectives and are able to identify measurable social outcomes from the particular government projects they are involved in. This would involve a project-by-project negotiation of goals and performance measures. It is likely to only be practical for large, longer term projects.

Smart buildings for healthy and sustainable workplace: scoping study report

Deficiencies in the design and operation of office buildings can give rise to high social, environmental and economic (triple bottom line) costs. As a result, there are significant pressures and incentives to develop ‘smart building’ technologies that can facilitate improved indoor environment quality (IEQ), and more energy efficient operation of office buildings. IEQ indicators include lighting, ventilation, thermal comfort, indoor air quality and noise. In response to this, the CRC for Construction Innovation commissioned a six-month scoping study (Project no. 2002-043) to examine how different technologies could be used to improve the ‘triple bottom line’ for office buildings. The study was supported by three industry partners, Bovis Lend Lease, Arup, and The Queensland Department of Public Works. The objective of the study was to look at the history, trends, drivers, new technologies and potential application areas related to the operation of healthy and efficient office buildings. The key output from the study was a recommendation for a prototype system for intelligent monitoring and control of an office environment, based on identified market, technical and user requirements and constraints.