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.

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.

Trading spaces : on the lore and limitations of latent semantic analysis

Two decades after its inception, Latent Semantic Analysis(LSA) has become part and parcel of every modern introduction to Information Retrieval. For any tool that matures so quickly, it is important to check its lore and limitations, or else stagnation will set in. We focus here on the three main aspects of LSA that are well accepted, and the gist of which can be summarized as follows: (1) that LSA recovers latent semantic factors underlying the document space, (2) that such can be accomplished through lossy compression of the document space by eliminating lexical noise, and (3) that the latter can best be achieved by Singular Value Decomposition. For each aspect we performed experiments analogous to those reported in the LSA literature and compared the evidence brought to bear in each case. On the negative side, we show that the above claims about LSA are much more limited than commonly believed. Even a simple example may show that LSA does not recover the optimal semantic factors as intended in the pedagogical example used in many LSA publications. Additionally, and remarkably deviating from LSA lore, LSA does not scale up well: the larger the document space, the more unlikely that LSA recovers an optimal set of semantic factors. On the positive side, we describe new algorithms to replace LSA (and more recent alternatives as pLSA, LDA, and kernel methods) by trading its l2 space for an l1 space, thereby guaranteeing an optimal set of semantic factors. These algorithms seem to salvage the spirit of LSA as we think it was initially conceived.

Information sharing with key suppliers : a contingency approach

This paper focuses on information sharing with key suppliers and seeks to explore the factors that might influence its extent and depth. We also investigate how information sharing affects a company’s performance with regards to resource usage, output, and flexibility. Drawing from transaction cost- and contingency theories, several factors, namely environmental uncertainty, demand uncertainty, dependency and, the product life cycle stage are proposed to explain the level of information shared with key suppliers. We develop a model where information sharing mediates the (contingent) factors and company performance. A mail survey was used to collect data from Finnish and Swedish companies. Partial Least Squares analysis was separately performed for each country (n=119, n=102). There was consistent evidence that environmental uncertainty, demand uncertainty and supplier/buyer dependency had explanatory power, whereas no significance was found for the product life cycle stage. The results also confirm previous studies by providing support for a positive relationship between information sharing and performance, where output performance was found to be the most strongly related

Determinants and effects of information sharing with key suppliers

This paper focuses on information sharing with key suppliers and seeks to explore the factors that might influence its extent and depth. We also investigate how information sharing affects a company’s performance with regards to resource usage, output, and flexibility. Drawing from transaction cost- and contingency theories, several factors, namely environmental uncertainty, demand uncertainty, dependency and, the product life cycle stage are proposed to explain the level of information shared with key suppliers. We develop a model where information sharing mediates the (contingent) factors and company performance. A mail survey was used to collect data from Finnish and Swedish companies. Partial Least Squares analysis was separately performed for each country (n=119, n=102). There was consistent evidence that environmental uncertainty, demand uncertainty and supplier/buyer dependency had explanatory power, whereas no significance was found for the relationship between product life cycle stage and information sharing. The results also confirm previous studies by providing support for a positive relationship between information sharing and performance, where output performance was found to be the most strongly related.

How quantum theory is developing the field of information retrieval

This position paper provides an overview of work conducted and an outlook of future directions within the field of Information Retrieval (IR) that aims to develop novel models, methods and frameworks inspired by Quantum Theory (QT).

An information accountability framework for shared eHealth policies

Privacy issues have hindered the evolution of e-health since its emergence. Patients demand better solutions for the protection of private information. Health professionals demand open access to patient health records. Existing e-health systems find it difficult to fulfill these competing requirements. In this paper, we present an information accountability framework (IAF) for e-health systems. The IAF is intended to address privacy issues and their competing concerns related to e-health. Capabilities of the IAF adhere to information accountability principles and e-health requirements. Policy representation and policy reasoning are key capabilities introduced in the IAF. We investigate how these capabilities are feasible using Semantic Web technologies. We discuss with the use of a case scenario, how we can represent the different types of policies in the IAF using the Open Digital Rights Language (ODRL).

Semantics-based web service discovery using information retrieval techniques

This paper demonstrates an experimental study that examines the accuracy of various information retrieval techniques for Web service discovery. The main goal of this research is to evaluate algorithms for semantic web service discovery. The evaluation is comprehensively benchmarked using more than 1,700 real-world WSDL documents from INEX 2010 Web Service Discovery Track dataset. For automatic search, we successfully use Latent Semantic Analysis and BM25 to perform Web service discovery. Moreover, we provide linking analysis which automatically links possible atomic Web services to meet the complex requirements of users. Our fusion engine recommends a final result to users. Our experiments show that linking analysis can improve the overall performance of Web service discovery. We also find that keyword-based search can quickly return results but it has limitation of understanding users’ goals.

A requirements framework for semantic business process modelling

The emergence of semantic technologies to deal with the underlying meaning of things, instead of a purely syntactical representation, has led to new developments in various fields, including business process modeling. Inspired by artificial intelligence research, technologies for semantic Web services have been proposed and extended to process modeling. However, the applicablility of semantic Web services for semantic business processes is limited because business processes encompass wider requirements of business than Web services. In particular, processes are concerned with the composition of tasks, that is, in which order activities are carried out, regardless of their implementation details; resources assigned to carry out tasks, such as machinery, people, and goods; data exchange; and security and compliance concerns.

Effects of enhanced somatosensory information on postural stability in older people and people with Parkinson’s disease

The somatosensory system plays an important role in balance control and age-related changes to this system have been implicated in falls. Parkinson’s disease (PD) is a chronic and progressive disease of the brain, characterized by postural instability and gait disturbance. Previous research has shown that deficiencies in somatosensory feedback may contribute to the poorer postural control demonstrated by PD individuals. However, few studies have comprehensively explored differences in somatosensory function and postural control between PD participants and healthy older individuals. The soles of the feet contain many cutaneous mechanoreceptors that provide important somatosensory information sources for postural control. Different types of insole devices have been developed to enhance this somatosensory information and improve postural stability, but these devices are often too complex and expensive to integrate into daily life. Textured insoles provide a more passive intervention that may be an inexpensive and accessible means to enhance the somatosensory input from the plantar surface of the feet. However, to date, there has been little work conducted to test the efficacy of enhanced somatosensory input induced by textured insoles in both healthy and PD populations during standing and walking. Therefore, the aims of this thesis were to determine: 1) whether textured insole surfaces can improve postural stability by enhancing somatosensory information in younger and older adults, 2) the differences between healthy older participants and PD participants for measures of physiological function and postural stability during standing and walking, 3) how changes in somatosensory information affect postural stability in both groups during standing and walking; and 4), whether textured insoles can improve postural stability in both groups during standing and walking. To address these aims, Study 1 recruited seven older individuals and ten healthy young controls to investigate the effects of two textured insole surfaces on postural stability while performing standing balance tests on a force plate. Participants were tested under three insole surface conditions: 1) barefoot; 2) standing on a hard textured insole surface; and 3), standing on a soft textured insole surface. Measurements derived from the centre of pressure displacement included the range of anterior-posterior and medial-lateral displacement, path length and the 90% confidence elliptical area (C90 area). Results of study 1 revealed a significant Group*Surface*Insole interaction for the four measures. Both textured insole surfaces reduced postural sway for the older group, especially in the eyes closed condition on the foam surface. However, participants reported that the soft textured insole surface was more comfortable and, hence, the soft textured insoles were adopted for Studies 2 and 3. For Study 2, 20 healthy older adults (controls) and 20 participants with Parkinson’s disease were recruited. Participants were evaluated using a series of physiological assessments that included touch sensitivity, vibratory perception, and pain and temperature threshold detection. Furthermore, nerve function and somatosensory evoked potentials tests were utilized to provide detailed information regarding peripheral nerve function for these participants. Standing balance and walking were assessed on different surfaces using a force plate and the 3D Vicon motion analysis system, respectively. Data derived from the force plate included the range of anterior-posterior and medial-lateral sway, while measures of stride length, stride period, cadence, double support time, stance phase, velocity and stride timing variability were reported for the walking assessment. The results of this study demonstrated that the PD group had decrements in somatosensory function compared to the healthy older control group. For electrodiagnosis, PD participants had poorer nerve function than controls, as evidenced by slower nerve conduction velocities and longer latencies in sural nerve and prolonged latency in the P37 somatosensory evoked potential. Furthermore, the PD group displayed more postural sway in both the anterior-posterior and medial-lateral directions relative to controls and these differences were increased when standing on a foam surface. With respect to the gait assessment, the PD group took shorter strides and had a reduced stride period compared with the control group. Furthermore, the PD group spent more time in the stance phase and had increased cadence and stride timing variability than the controls. Compared with walking on the firm surface, the two groups demonstrated different gait adaptations while walking on the uneven surface. Controls increased their stride length and stride period and decreased their cadence, which resulted in a consistent walking velocity on both surfaces. Conversely, while the PD patients also increased their stride period and decreased their cadence and stance period on the uneven surface, they did not increase their stride length and, hence walked slower on the uneven surface. In the PD group, there was a strong positive association between decreased somatosensory function and decreased clinical balance, as assessed by the Tinetti test. Poorer somatosensory function was also strongly positively correlated with the temporospatial gait parameters, especially shorter stride length. Study 3 evaluated the effects of manipulating the somatosensory information from the plantar surface of the feet using textured insoles in the same populations assessed in Study 2. For this study, participants performed the standing and walking balance tests under three footwear conditions: 1) barefoot; 2) with smooth insoles; and 3), with textured insoles. Standing balance and walking were evaluated using a force plate and a Vicon motion analysis system and the data were analysed in the same way outlined for Study 2. The findings showed that the smooth and textured insoles caused different effects on postural control during both the standing and walking trials. Both insoles decreased medial-lateral sway to the same level on the firm surface. The greatest benefits were observed in the PD group while wearing the textured insole. When standing under a more challenging condition on the foam surface with eyes closed, only the textured insole decreased medial-lateral sway in the PD group. With respect to the gait trials, both insoles increased walking velocity, stride length and stride time and decreased cadence, but these changes were more pronounced for the textured insoles. The effects of the textured insoles were evident under challenging conditions in the PD group and increased walking velocity and stride length, while decreasing cadence. Textured insoles were also effective in reducing the time spent in the double support and stance phases of the gait cycle and did not increase stride timing variability, as was the case for the smooth insoles for the PD group. The results of this study suggest that textured insoles, such as those evaluated in this research, may provide a low-cost means of improving postural stability in high-risk groups, such as people with PD, which may act as an important intervention to prevent falls.

A tensor encoding model for semantic processing

This paper develops and evaluates an enhanced corpus based approach for semantic processing. Corpus based models that build representations of words directly from text do not require pre-existing linguistic knowledge, and have demonstrated psychologically relevant performance on a number of cognitive tasks. However, they have been criticised in the past for not incorporating sufficient structural information. Using ideas underpinning recent attempts to overcome this weakness, we develop an enhanced tensor encoding model to build representations of word meaning for semantic processing. Our enhanced model demonstrates superior performance when compared to a robust baseline model on a number of semantic processing tasks.

Real, complex, and binary semantic vectors

This paper presents a combined structure for using real, complex, and binary valued vectors for semantic representation. The theory, implementation, and application of this structure are all significant. For the theory underlying quantum interaction, it is important to develop a core set of mathematical operators that describe systems of information, just as core mathematical operators in quantum mechanics are used to describe the behavior of physical systems. The system described in this paper enables us to compare more traditional quantum mechanical models (which use complex state vectors), alongside more generalized quantum models that use real and binary vectors. The implementation of such a system presents fundamental computational challenges. For large and sometimes sparse datasets, the demands on time and space are different for real, complex, and binary vectors. To accommodate these demands, the Semantic Vectors package has been carefully adapted and can now switch between different number types comparatively seamlessly. This paper describes the key abstract operations in our semantic vector models, and describes the implementations for real, complex, and binary vectors. We also discuss some of the key questions that arise in the field of quantum interaction and informatics, explaining how the wide availability of modelling options for different number fields will help to investigate some of these questions.

Empirical analysis of the effect of dimension reduction and word order on semantic vectors

The aim of this paper is to provide a comparison of various algorithms and parameters to build reduced semantic spaces. The effect of dimension reduction, the stability of the representation and the effect of word order are examined in the context of the five algorithms bearing on semantic vectors: Random projection (RP), singular value decom- position (SVD), non-negative matrix factorization (NMF), permutations and holographic reduced representations (HRR). The quality of semantic representation was tested by means of synonym finding task using the TOEFL test on the TASA corpus. Dimension reduction was found to improve the quality of semantic representation but it is hard to find the optimal parameter settings. Even though dimension reduction by RP was found to be more generally applicable than SVD, the semantic vectors produced by RP are somewhat unstable. The effect of encoding word order into the semantic vector representation via HRR did not lead to any increase in scores over vectors constructed from word co-occurrence in context information. In this regard, very small context windows resulted in better semantic vectors for the TOEFL test.