Improved collaborative filtering using clustering and association rule mining on implicit data

The recommender systems are recently becoming more significant due to their ability in making decisions on appropriate choices. Collaborative Filtering (CF) is the most successful and most applied technique in the design of a recommender system where items to an active user will be recommended based on the past rating records from like-minded users. Unfortunately, CF may lead to poor recommendation when user ratings on items are very sparse (insufficient number of ratings) in comparison with the huge number of users and items in user-item matrix. In the case of a lack of user rating on items, implicit feedback is used to profile a user’s item preferences. Implicit feedback can indicate users’ preferences by providing more evidences and information through observations made on users’ behaviors. Data mining technique, which is the focus of this research, can predict a user’s future behavior without item evaluation and can too, analyze his preferences. In order to investigate the states of research in CF and implicit feedback, a systematic literature review has been conducted on the published studies related to topic areas in CF and implicit feedback. To investigate users’ activities that influence the recommender system developed based on the CF technique, a critical observation on the public recommendation datasets has been carried out. To overcome data sparsity problem, this research applies users’ implicit interaction records with items to efficiently process massive data by employing association rules mining (Apriori algorithm). It uses item repetition within a transaction as an input for association rules mining, in which can achieve high recommendation accuracy. To do this, a modified preprocessing has been employed to discover similar interest patterns among users. In addition, the clustering technique (Hierarchical clustering) has been used to reduce the size of data and dimensionality of the item space as the performance of association rules mining. Then, similarities between items based on their features have been computed to make recommendations. Experiments have been conducted and the results have been compared with basic CF and other extended version of CF techniques including K-Means Clustering, Hybrid Representation, and Probabilistic Learning by using public dataset, namely, Million Song dataset. The experimental results demonstrate that the proposed technique exhibits improvements of an average of 20% in terms of Precision, Recall and Fmeasure metrics when compared to the basic CF technique. Our technique achieves even better performance (an average of 15% improvement in terms of Precision and Recall metrics) when compared to the other extended version of CF techniques, even when the data is very sparse

Search Results: Predicting Ranking Algorithms With User Ratings and User-Driven Data

The purpose of this correlational quantitative study was to examine the possible relationship between user-driven parameters, user ratings, and ranking algorithms. The study’s population consisted of students and faculty in the information technology (IT) field at a university in Huntington, WV. Arrow’s impossibility theorem was used as the theoretical framework for this study. Complete survey data were collected from 47 students and faculty members in the IT field, and a multiple regression analysis was used to measure the correlations between the variables. The model was able to explain 85% of the total variability in the ranking algorithm. The overall model was able to significantly predict the algorithm ranking discounted cumulative gain, R2 = .852, F(3,115) = 220.13, p \u3c .01. The Respondent DCG and Search term variables were the most significant predictor with p = .0001. The overall findings can potentially be useful to content providers who focus their content on a specific niche. The content created by these providers would most likely be focused entirely on that subgroup of interested users. While it is necessary to focus content to the interested users, it may be beneficial to expand the content to more generic terms to help reach potential new users outside of the subgroups of interest. User’s searching for more generic terms could potentially be exposed to more content that would generally require more specific search terms. This exposure with more generic terms could help users expand their knowledge of new content more quickly

Context-Aware Recommendation Systems in Mobile Environments

Nowadays, the huge amount of information available may easily overwhelm users when they need to take a decision that involves choosing among several options. As a solution to this problem, Recommendation Systems (RS) have emerged to offer relevant items to users. The main goal of these systems is to recommend certain items based on user preferences. Unfortunately, traditional recommendation systems do not consider the user’s context as an important dimension to ensure high-quality recommendations. Motivated by the need to incorporate contextual information during the recommendation process, Context-Aware Recommendation Systems (CARS) have emerged. However, these recent recommendation systems are not designed with mobile users in mind, where the context and the movements of the users and items may be important factors to consider when deciding which items should be recommended. Therefore, context-aware recommendation models should be able to effectively and efficiently exploit the dynamic context of the mobile user in order to offer her/him suitable recommendations and keep them up-to-date.The research area of this thesis belongs to the fields of context-aware recommendation systems and mobile computing. We focus on the following scientific problem: how could we facilitate the development of context-aware recommendation systems in mobile environments to provide users with relevant recommendations? This work is motivated by the lack of generic and flexible context-aware recommendation frameworks that consider aspects related to mobile users and mobile computing. In order to solve the identified problem, we pursue the following general goal: the design and implementation of a context-aware recommendation framework for mobile computing environments that facilitates the development of context-aware recommendation applications for mobile users. In the thesis, we contribute to bridge the gap not only between recommendation systems and context-aware computing, but also between CARS and mobile computing.<br /

Enhanced Living Environments

This open access book was prepared as a Final Publication of the COST Action IC1303 “Algorithms, Architectures and Platforms for Enhanced Living Environments (AAPELE)”. The concept of Enhanced Living Environments (ELE) refers to the area of Ambient Assisted Living (AAL) that is more related with Information and Communication Technologies (ICT). Effective ELE solutions require appropriate ICT algorithms, architectures, platforms, and systems, having in view the advance of science and technology in this area and the development of new and innovative solutions that can provide improvements in the quality of life for people in their homes and can reduce the financial burden on the budgets of the healthcare providers. The aim of this book is to become a state-of-the-art reference, discussing progress made, as well as prompting future directions on theories, practices, standards, and strategies related to the ELE area. The book contains 12 chapters and can serve as a valuable reference for undergraduate students, post-graduate students, educators, faculty members, researchers, engineers, medical doctors, healthcare organizations, insurance companies, and research strategists working in this area

Exploiting tag information for search and personalization

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Middleware for Mobile Sensing Applications in Urban Environments

Sensor networks represent an attractive tool to observe the physical world. Networks of tiny sensors can be used to detect a fire in a forest, to monitor the level of pollution in a river, or to check on the structural integrity of a bridge. Application-specific deployments of static-sensor networks have been widely investigated. Commonly, these networks involve a centralized data-collection point and no sharing of data outside the organization that owns it. Although this approach can accommodate many application scenarios, it significantly deviates from the pervasive computing vision of ubiquitous sensing where user applications seamlessly access anytime, anywhere data produced by sensors embedded in the surroundings. With the ubiquity and ever-increasing capabilities of mobile devices, urban environments can help give substance to the ubiquitous sensing vision through Urbanets, spontaneously created urban networks. Urbanets consist of mobile multi-sensor devices, such as smart phones and vehicular systems, public sensor networks deployed by municipalities, and individual sensors incorporated in buildings, roads, or daily artifacts. My thesis is that "multi-sensor mobile devices can be successfully programmed to become the underpinning elements of an open, infrastructure-less, distributed sensing platform that can bring sensor data out of their traditional close-loop networks into everyday urban applications". Urbanets can support a variety of services ranging from emergency and surveillance to tourist guidance and entertainment. For instance, cars can be used to provide traffic information services to alert drivers to upcoming traffic jams, and phones to provide shopping recommender services to inform users of special offers at the mall. Urbanets cannot be programmed using traditional distributed computing models, which assume underlying networks with functionally homogeneous nodes, stable configurations, and known delays. Conversely, Urbanets have functionally heterogeneous nodes, volatile configurations, and unknown delays. Instead, solutions developed for sensor networks and mobile ad hoc networks can be leveraged to provide novel architectures that address Urbanet-specific requirements, while providing useful abstractions that hide the network complexity from the programmer. This dissertation presents two middleware architectures that can support mobile sensing applications in Urbanets. Contory offers a declarative programming model that views Urbanets as a distributed sensor database and exposes an SQL-like interface to developers. Context-aware Migratory Services provides a client-server paradigm, where services are capable of migrating to different nodes in the network in order to maintain a continuous and semantically correct interaction with clients. Compared to previous approaches to supporting mobile sensing urban applications, our architectures are entirely distributed and do not assume constant availability of Internet connectivity. In addition, they allow on-demand collection of sensor data with the accuracy and at the frequency required by every application. These architectures have been implemented in Java and tested on smart phones. They have proved successful in supporting several prototype applications and experimental results obtained in ad hoc networks of phones have demonstrated their feasibility with reasonable performance in terms of latency, memory, and energy consumption.Deploying a network of sensors to monitor an environment is a common practice. For example, cameras in museums, supermarkets, or buildings are installed for surveillance purposes. However, while a decade ago, most deployed sensor networks involved a limited number of sensors, wired to a central processing unit, nowadays, the focus is on wireless, distributed, sensing nodes. Sensor technology has greatly advanced in terms of size, power consumption, processing capabilities, and low cost, thus fostering deployments of self-organizing wireless sensor networks over large geographical areas. For example, sensor networks have been used to detect a fire in a forest, to monitor the level of pollution in a river, or to check on the structural integrity of a bridge. Yet, sensor networks are usually perceived as ``something'' remote in the forest or on the battlefield, and regular users do not yet benefit from them. With the ubiquity and ever-increasing capabilities of mobile devices, such as smart phones and computers embedded in cars, urban environments offer the elements necessary to create people-centric mobile sensor networks and support a large variety of so-called sensing applications ranging from emergency and surveillance to tourist guidance and entertainment. For example, near-ubiquitous smart phones with audio and video sensing capabilities and more sensors in the near future can be used to provide shopping recommender services to inform users of special offers at the mall. Sensor-equipped cars can be used to provide traffic information services to alert drivers to upcoming traffic jams. However, urban mobile sensor networks are challenging programming environments due to the dynamism of mobile devices, the resource constraints of battery-powered devices, the software and hardware heterogeneity, and the large number of concurrent applications that they need to support. These requirements hinder the direct adoption of traditional distributed computing platforms developed for static resource-rich networks. This dissertation presents two architectures that can support the development of mobile sensing applications in urban environments. Contory offers a declarative programming model that views the urban network as a distributed sensor database. Context-aware Migratory Services provides a client-server paradigm, where services are capable of migrating to different nodes in the network in order to maintain a continuous interaction with clients. Compared to previous approaches to supporting mobile sensing urban applications, our architectures are entirely distributed and do not assume constant availability of Internet connectivity. These architectures have been implemented in Java and tested on smart phones. They have proved successful in supporting several prototype applications and experimental results obtained in networks of phones have demonstrated their feasibility with reasonable performance in terms of latency, memory, and energy consumption. The proposed architectures offer many opportunities to flexibly and quickly establish customized services that can greatly enhance the users' urban experience. Further steps to fully accomplish people-centric mobile sensing applications will have to address more technical issues as well as social and legal concerns

Powering the Academic Web

Context: Locating resources on the Web has become increasingly difficult for users and poses a number of issues. The sheer size of the Web means that despite what appears to be an increase in the amount of quality material available, the effort involved in locating that material is also increasing; in effect, the higher quality material is being diluted by the lesser quality. One such group affected by this problem is post-graduate students. Having only a finite amount of time to devote to research, this reduces their overall quality study time. Aim: This research investigates how post-graduate students use the Web as a learning resource and identifies a number of areas of concern with its use. It considers the potential for improvement in this matter by using a number of concepts such as: collaboration; peer reviewing and document classification and comparison techniques. This research also investigates whether by combining several of the identified technologies and concepts, student research on the Web can be improved. Method: Using some of the identified concepts as components, this research proposes a model to address the highlighted areas of concern. The proposed model, named the Durham Browsing Assistant (DurBA) is defined, and a number of key concepts which show potential within it are uncovered. One of the key concepts is chosen, that of document comparison. Given a source document, can a computer system reliably identify other documents which most closely match it from other on the Web? A software tool was created which allowed the testing of document comparison techniques, this was called the Durham Textual Comparison system (DurTeC) and it had two key concepts. The first was that it would allow various algorithms to be applied to the comparison process. The second concept was that it could simulate collaboration by allowing data to be altered, added and removed as if by multiple users. A set of experiments were created to test these algorithms and identify those which gave the best results. Results: The results from the experiments identified a number of the most promising relationships between comparison and collaboration processes. It also highlighted those which had a negative effect on the process, and those which produced variable results. Amongst the results, it was found that: 1. By providing DurTeC with additional source documents to the original, as if through a recommendation process, it was able to increase its accuracy substantially. 2. By allowing DurTeC to use synonym lists to expand its vocabulary, in many cases, it was found to have reduced its accuracy. 3. By restricting those words which DurTeC considered in its comparison process, based upon their value in the source document, accuracy could be increased. This could be considered as a form of collaborative keyword selection. Conclusion: This research shows that improvements can be made in the accuracy of identifying similar resources by using a combination of comparison and collaboration processes. The proposed model, DurBA would be an ideal host for such a system

Security in Distributed, Grid, Mobile, and Pervasive Computing

This book addresses the increasing demand to guarantee privacy, integrity, and availability of resources in networks and distributed systems. It first reviews security issues and challenges in content distribution networks, describes key agreement protocols based on the Diffie-Hellman key exchange and key management protocols for complex distributed systems like the Internet, and discusses securing design patterns for distributed systems. The next section focuses on security in mobile computing and wireless networks. After a section on grid computing security, the book presents an overview of security solutions for pervasive healthcare systems and surveys wireless sensor network security

The Cloud-to-Thing Continuum

The Internet of Things offers massive societal and economic opportunities while at the same time significant challenges, not least the delivery and management of the technical infrastructure underpinning it, the deluge of data generated from it, ensuring privacy and security, and capturing value from it. This Open Access Pivot explores these challenges, presenting the state of the art and future directions for research but also frameworks for making sense of this complex area. This book provides a variety of perspectives on how technology innovations such as fog, edge and dew computing, 5G networks, and distributed intelligence are making us rethink conventional cloud computing to support the Internet of Things. Much of this book focuses on technical aspects of the Internet of Things, however, clear methodologies for mapping the business value of the Internet of Things are still missing. We provide a value mapping framework for the Internet of Things to address this gap. While there is much hype about the Internet of Things, we have yet to reach the tipping point. As such, this book provides a timely entrée for higher education educators, researchers and students, industry and policy makers on the technologies that promise to reshape how society interacts and operates