User behaviour modelling in a multi-dimensional environment for personalization and recommendation

Handling information overload online, from the user's point of view is a big challenge, especially when the number of websites is growing rapidly due to growth in e-commerce and other related activities. Personalization based on user needs is the key to solving the problem of information overload. Personalization methods help in identifying relevant information, which may be liked by a user. User profile and object profile are the important elements of a personalization system. When creating user and object profiles, most of the existing methods adopt two-dimensional similarity methods based on vector or matrix models in order to find inter-user and inter-object similarity. Moreover, for recommending similar objects to users, personalization systems use the users-users, items-items and users-items similarity measures. In most cases similarity measures such as Euclidian, Manhattan, cosine and many others based on vector or matrix methods are used to find the similarities. Web logs are high-dimensional datasets, consisting of multiple users, multiple searches with many attributes to each. Two-dimensional data analysis methods may often overlook latent relationships that may exist between users and items. In contrast to other studies, this thesis utilises tensors, the high-dimensional data models, to build user and object profiles and to find the inter-relationships between users-users and users-items. To create an improved personalized Web system, this thesis proposes to build three types of profiles: individual user, group users and object profiles utilising decomposition factors of tensor data models. A hybrid recommendation approach utilising group profiles (forming the basis of a collaborative filtering method) and object profiles (forming the basis of a content-based method) in conjunction with individual user profiles (forming the basis of a model based approach) is proposed for making effective recommendations. A tensor-based clustering method is proposed that utilises the outcomes of popular tensor decomposition techniques such as PARAFAC, Tucker and HOSVD to group similar instances. An individual user profile, showing the user's highest interest, is represented by the top dimension values, extracted from the component matrix obtained after tensor decomposition. A group profile, showing similar users and their highest interest, is built by clustering similar users based on tensor decomposed values. A group profile is represented by the top association rules (containing various unique object combinations) that are derived from the searches made by the users of the cluster. An object profile is created to represent similar objects clustered on the basis of their similarity of features. Depending on the category of a user (known, anonymous or frequent visitor to the website), any of the profiles or their combinations is used for making personalized recommendations. A ranking algorithm is also proposed that utilizes the personalized information to order and rank the recommendations. The proposed methodology is evaluated on data collected from a real life car website. Empirical analysis confirms the effectiveness of recommendations made by the proposed approach over other collaborative filtering and content-based recommendation approaches based on two-dimensional data analysis methods.

IS service quality as a multi-dimensional formative constuct

This study is motivated by, and proceeds from, a central interest in the importance of evaluating IS service quality and adopts the IS ZOT SERVQUAL instrument (Kettinger & Lee, 2005) as its core theory base. This study conceptualises IS service quality as a multidimensional formative construct and seeks to answer the main research questions: “Is the IS service quality construct valid as a 1st-order formative, 2nd-order formative multidimensional construct?” Additionally, with the aim of validating the IS service quality construct within its nomological net, as in prior service marketing work, Satisfaction was hypothesised as its immediate consequence. With the goal of testing the above research question, IS service quality and Satisfaction were operationalised in a quantitative survey instrument. Partial least squares (PLS), employing 219 valid responses, largely evidenced the validity of IS service quality as a multidimensional formative construct. The nomological validity of the IS service quality construct was also evidenced by demonstrating that 55% of Satisfaction was explained by the multidimensional formative IS service quality construct.

Scalable Context-Sensitive Points-To Analysis Using Multi-Dimensional Bloom Filters

Context-sensitive points-to analysis is critical for several program optimizations. However, as the number of contexts grows exponentially, storage requirements for the analysis increase tremendously for large programs, making the analysis non-scalable. We propose a scalable flow-insensitive context-sensitive inclusion-based points-to analysis that uses a specially designed multi-dimensional bloom filter to store the points-to information. Two key observations motivate our proposal: (i) points-to information (between pointer-object and between pointer-pointer) is sparse, and (ii) moving from an exact to an approximate representation of points-to information only leads to reduced precision without affecting correctness of the (may-points-to) analysis. By using an approximate representation a multi-dimensional bloom filter can significantly reduce the memory requirements with a probabilistic bound on loss in precision. Experimental evaluation on SPEC 2000 benchmarks and two large open source programs reveals that with an average storage requirement of 4MB, our approach achieves almost the same precision (98.6%) as the exact implementation. By increasing the average memory to 27MB, it achieves precision upto 99.7% for these benchmarks. Using Mod/Ref analysis as the client, we find that the client analysis is not affected that often even when there is some loss of precision in the points-to representation. We find that the NoModRef percentage is within 2% of the exact analysis while requiring 4MB (maximum 15MB) memory and less than 4 minutes on average for the points-to analysis. Another major advantage of our technique is that it allows to trade off precision for memory usage of the analysis.

Partition function for hindered, one-and multi-dimensional rotors

Writing the hindered rotor (hr) partition function as the trace of (rho) over cap = e(-beta(H) over cap hr), we approximate it by the sum of contributions from a set of points in position space. The contribution of the density matrix from each point is approximated by performing a local harmonic expansion around it. The highlight of this method is that it can be easily extended to multidimensional systems. Local harmonic expansion leads to a breakdown of the method a low temperatures. In order to calculate the partition function at low temperatures, we suggest a matrix multiplication procedure. The results obtained using these methods closely agree with the exact partition function at all temperature ranges. Our method bypasses the evaluation of eigenvalues and eigenfunctions and evaluates the density matrix for internal rotation directly. We also suggest a procedure to account for the antisymmetry of the total wavefunction in the same. (C) 2012 Elsevier B.V. All rights reserved.

Scalable black-box prediction models for multi-dimensional adaptation on NUMA multi-cores

This paper presents a scalable, statistical ‘black-box’ model for predicting the performance of parallel programs on multi-core non-uniform memory access (NUMA) systems. We derive a model with low overhead, by reducing data collection and model training time. The model can accurately predict the behaviour of parallel applications in response to changes in their concurrency, thread layout on NUMA nodes, and core voltage and frequency. We present a framework that applies the model to achieve significant energy and energy-delay-square (ED2) savings (9% and 25%, respectively) along with performance improvement (10% mean) on an actual 16-core NUMA system running realistic application workloads. Our prediction model proves substantially more accurate than previous efforts.

Lower bounds for stability margin of n-dimensional discrete systems

This paper derives lower bounds for the stability margin of n-dimensional discrete systems in the Roesser’s state space setting. The lower bounds for stability margin are derived based on the MacLaurine series expansion. Numerical examples are given to illustrate the results.

The national mapping of teacher professional learning project : a multi-dimensional space?

This essay focuses on the National Mapping of Teacher Professional Learning (2008), a report that we co-authored along with a number of other researchers on the basis of extensive surveys and interviews relating to the policies and practices of teacher professional learning in Australia. The report is an update of an earlier survey conducted by David McRae and others, entitled PD 2000, and it registers significant changes in attitudes and practices relating to professional learning across Australia in the intervening period. Perhaps the most significant development is the way professional learning is now recognized as an important vehicle for education reform by systems, schools and by teachers themselves, most notably the standards-based reforms that have such a decisive effect on the policy landscape here in Australia and in other countries. The work of the AATE in developing the Standards for Teachers of English Language and Literacy (STELLA) is mentioned in the report. It was acknowledged that STELLA provides a generative framework for professional learning, sometimes in contradiction to more managerial approaches. The question remains, however, of how English teachers as a professional community might locate themselves within the policy landscape described in this report. This essay is an attempt to promote this kind of discussion and to argue the distinctive nature of the standpoint that English teachers might bring to thinking about and planning for professional learning and practitioner inquiry.

Visiview : a system for the visualization of multi-dimensional data

Results generated by simulation of computer systems are often presented as a multi-dimensional data set, where the number of dimensions may be greater than 4 if sufficient system parameters are modelled. This paper describes a visualization system intended to assist in understanding the relationship between, and effect upon system behavior of, the different values of the system parameters.

The system is applied to data that cannot be represented using a mesh or isosurface representation, and in general can only be represented as a cloud of points. The use of stereoscopic rendering and rapid interaction with the data are compared with regard to their value in providing insight into the nature of the data.

A number of techniques are implemented for displaying projections of the data set with up to 7 dimensions, and for allowing intuitive manipulation of the remaining dimensions. In this way the effect of changes in one variable in the presence of a number of others can be explored.

The use of these techniques, when applied to data from computer system simulation, results in an intuitive understanding of the effects of the system parameters on system behavior.

Multi-dimensional effects on Cladoceran (Crustacea, Anomopoda) assemblages in two cascade reservoirs in Southeast Brazil

The Cladocera assemblages in two cascade reservoirs located in the Paranapanema River in Brazil were studied during two consecutive years. Upstream Chavantes Reservoir is an accumulation system, with a long water retention time, high depth and oligo-mesotrophic status. The downstream Salto Grande Reservoir is a small, run-of-river reservoir, with a short water retention time, shallow depth and meso-eutrophic status. The goal of this study was to determine the inter- and intra-reservoir limnological differences with emphasis on the Cladocerans assemblages. The following questions were posed: (i) what are the seasonal dynamics of the reservoir spatial structures; (ii) how dynamics, seasonally, is the reservoirs spatial structure; and (iii) are the reservoir independent systems? A total of 43 Cladoceran species were identified in this study. Ceriodaphnia silvestrii was the most abundant and frequent species found in Chavantes Reservoir, while C. cornuta was most abundant and frequent in Salto Grande Reservoir. The Cladoceran species richness differed significantly among sampling sites for both reservoirs. In terms of abundance, there was a significant variation among sampling sites and periods for both reservoirs. A cluster analysis indicated a higher similarity among the deeper compartments, and the intermediate river-reservoir zones was grouped with the riverine sampling sites. For the smaller Salto Grande Reservoir, the entrance of a middle size tributary causes major changes in the system. A distinct environment was observed in the river mouth zone of another small tributary, representing a shallow environment with aquatic macrophyte stands. A canonical correlation analysis between environmental variables and Cladoceran abundance explained 75% of the data variability, and a complementary factorial analysis explained 65% of the variability. The spatial compartmentalization of the reservoirs, as well as the particular characteristics of the two study reservoirs, directly influenced the structure of the Cladoceran assemblages. The conditions of the lacustrine (dam) zone of the larger Chavantes Reservoir were reflected in the upstream zone of the smaller downstream Salto Grande Reservoir, highlighting the importance of plankton exportation in reservoir cascade systems. The comparative spatial-temporal analysis indicated conspicuous differences between the two reservoirs, reinforcing the necessity of considering tropical/subtropical reservoirs as complex, multi-compartmental water systems. © 2010 The Authors. Journal compilation © 2010 Blackwell Publishing Asia Pty Ltd.

Flexibility and modeling in business processes: a multi-dimensional relationship

Over the last few years, Business Process Management (BPM) has achieved increasing popularity and dissemination. An analysis of the underlying assumptions of BPM shows that it pursues two apparently contradicting goals: on the one hand it aims at formalising work practices into business process models; on the other hand, it intends to confer flexibility to the organization - i.e. to maintain its ability to respond to new and unforeseen situations. This paper analyses the relationship between formalisation and flexibility in business process modelling by means of an empirical case study of a BPM project in an aircraft maintenance company. A qualitative approach is adopted based on the Actor-Network Theory. The paper offers two major contributions: (a) it illustrates the sociotechnical complexity involved in BPM initiatives; (b) it points towards a multidimensional understanding of the relation between formalization and flexibility in BPM projects.