Cross domain recommender systems using matrix and tensor factorization

Today, the amount and importance of available data on the internet are growing exponentially. These digital data has become a primary source of information and the people’s life bonded to them tightly. The data comes in diverse shapes and from various resources and users utilize them in almost all their personal or social activities. However, selecting a desirable option from the huge list of available options can be really frustrating and time-consuming. Recommender systems aim to ease this process by finding the proper items which are more likely to be interested by users. Undoubtedly, there is not even one social media or online service which can continue its’ work properly without using recommender systems. On the other hand, almost all available recommendation techniques suffer from some common issues: the data sparsity, the cold-start, and the new-user problems. This thesis tackles the mentioned problems using different methods. While, most of the recommender methods rely on using single domain information, in this thesis, the main focus is on using multi-domain information to create cross-domain recommender systems. A cross-domain recommender system is not only able to handle the cold-start and new-user situations much better, but it also helps to incorporate different features exposed in diverse domains together and capture a better understanding of the users’ preferences which means producing more accurate recommendations. In this thesis, a pre-clustering stage is proposed to reduce the data sparsity as well. Various cross-domain knowledge-based recommender systems are suggested to recommend items in two popular social media, the Twitter and LinkedIn, by using different information available in both domains. The state of art techniques in this field, namely matrix factorization and tensor decomposition, are implemented to develop cross-domain recommender systems. The presented recommender systems based on the coupled nonnegative matrix factorization and PARAFAC-style tensor decomposition are evaluated using real-world datasets and it is shown that they superior to the baseline matrix factorization collaborative filtering. In addition, network analysis is performed on the extracted data from Twitter and LinkedIn

An architecture for user preference-based IoT service selection in cloud computing using mobile devices for smart campus

The Internet of things refers to the set of objects that have identities and virtual personalities operating in smart spaces using intelligent interfaces to connect and communicate within social environments and user context. Interconnected devices communicating to each other or to other machines on the network have increased the number of services. The concepts of discovery, brokerage, selection and reliability are important in dynamic environments. These concepts have emerged as an important field distinguished from conventional distributed computing by its focus on large-scale resource sharing, delivery and innovative applications. The usage of Internet of Things technology across different service provisioning environments has increased the challenges associated with service selection and discovery. Although a set of terms can be used to express requirements for the desired service, a more detailed and specific user interface would make it easy for the users to express their requirements using high-level constructs. In order to address the challenge of service selection and discovery, we developed an architecture that enables a representation of user preferences and manipulates relevant descriptions of available services. To ensure that the key components of the architecture work, algorithms (content-based and collaborative filtering) derived from the architecture were proposed. The architecture was tested by selecting services using content-based as well as collaborative algorithms. The performances of the algorithms were evaluated using response time. Their effectiveness was evaluated using recall and precision. The results showed that the content-based recommender system is more effective than the collaborative filtering recommender system. Furthermore, the results showed that the content-based technique is more time-efficient than the collaborative filtering technique

Reducing edible food waste in the UK food manufacturing supply chain through collaboration

A thesis submitted to the University of Bedfordshire, in fulfilment of the requirements for the degree of Master of Philosophy.The aim of this study is to examine the relationship between food manufacturing supply chain (FMSC) collaboration, collaborative effectiveness and edible food waste (EF) waste reduction; and also identify the key dimensions of collaboration and collaborative effectiveness in the context of the FMSC. A conceptual framework was built based on thorough relevant literature review and theory. Then all items of the conceptual framework were revised by academics and practitioners. The model was empirically tested with survey data using 122 responses from food manufacturing firms, using PLS-SEM. The findings indicated that the structural paths support hypotheses that FMSC collaboration has a positive effect related to collaborative effectiveness, and collaborative effectiveness has a strong contribution in EF waste (over-production of EF waste, processing of EF waste and storage of EF waste) reduction. However, the direct impact of FMSC collaboration on EF waste (over-production of EF waste, processing of EF waste and storage of EF waste) reduction is insignificant. A mediation analysis showed that the relationship between FMSC collaboration and EF waste is fully mediated by collaborative effectiveness. This research brought relational view theory for the concept of FMSC collaboration and collaborative effectiveness into the FMSC context, which has not previously been done, and developed and validated those constructs and relationships. The UK FMSC members would benefit from applying all dimensions of FMSC collaboration in this study to their supply chain operation to achieve greater collaborative effectiveness, and that will lead to reducing EF waste

Expanding the Horizons of Manufacturing: Towards Wide Integration, Smart Systems and Tools

This research topic aims at enterprise-wide modeling and optimization (EWMO) through the development and application of integrated modeling, simulation and optimization methodologies, and computer-aided tools for reliable and sustainable improvement opportunities within the entire manufacturing network (raw materials, production plants, distribution, retailers, and customers) and its components. This integrated approach incorporates information from the local primary control and supervisory modules into the scheduling/planning formulation. That makes it possible to dynamically react to incidents that occur in the network components at the appropriate decision-making level, requiring fewer resources, emitting less waste, and allowing for better responsiveness in changing market requirements and operational variations, reducing cost, waste, energy consumption and environmental impact, and increasing the benefits. More recently, the exploitation of new technology integration, such as through semantic models in formal knowledge models, allows for the capture and utilization of domain knowledge, human knowledge, and expert knowledge toward comprehensive intelligent management. Otherwise, the development of advanced technologies and tools, such as cyber-physical systems, the Internet of Things, the Industrial Internet of Things, Artificial Intelligence, Big Data, Cloud Computing, Blockchain, etc., have captured the attention of manufacturing enterprises toward intelligent manufacturing systems

What are the key determinants of an effective business process management in a logistics company and assess how best a strategic business management framework can be developed in order to enhance service excellence: Case study of Tranex Express Nigeria?

The term process management in logistics activities has evolved over the years because the centralization of business process through the application of ERP has enabled firms manage their day-to-day activities seamlessly. Although companies reporting platforms have integrated into their supply chain management the benefits of this process will be linked into service excellence which enables firm to enjoy positive and dynamic process flow. Therefore, to evaluate how firms enjoy this positive and dynamic benefits thesis literature review showcased the essence of enterprise resource planning (ERP) in business process management; other forms of business process (BP) were reviewed some of which are lean and six-sigma, however, research focused more on ERP and how it has been applied in integrating business processes from production to distribution of products through logistics. This study provided a thorough understanding of Tranex Logistics and their ability to achieve their long-term mission and vision; also, their ability to become customer/employee and investor of choice is tested by their ability to sustain their business structure without restricting information flow, partnership working and agility across all logistics activities. Basic elements which have been assessed in this thesis are related to data management and impact of information technology (IT); thesis evaluated three frameworks of ERP which are visibility, collaboration, and agility. Finally, thesis examined how business process management has evolved from manual ways of working to centralized system of process management with the aid of software applications/ERP and how business threats are being managed in Tranex Logistics; This thesis will access the role of ERP in logistic business process management in Covid-19 Pandemic which shows that ERP enabled firms to re-strategize during the pandemic and showcased the importance of ERP by demonstrating how it assisted firms in sustaining their process. To achieve the aims and objectives of this study author coordinated a qualitative research in Tranex Logistics by collating empirical data from among 29 employees:→subordinate and middle level managers/superordinate some of which are IT manager/finance manager while some of the subordinates are drivers, tracking officers, customer care and workshop repairers; tool applied in data collection are in-depth interviews. Data collected were analysed through the aid of thematic analysis and the core themes derived are: -1) information flow 2) partnership working 3) agility 4) service excellence and 5) revenue. Various themes were analysed, and investigation enabled researcher develop themes which provided a landmark to the importance of business process management based on data analysed. The research result outcome showcased the main strategic business management framework which has enhanced Tranex Logistics to incorporate ERP; thus, the effect of business process in Tranex Logistics has enabled them increase and monitor revenue; develop strategic business method of partnership working and above all increase information flow across various organization silos through the aid of artificial intelligence/ERP they are able to monitor and manage big data analytics and ensure information accuracy across all platforms which has enabled them build accurate market intelligence which has facilitated effective customer excellence. This study will enable Tranex Logistics identify areas whereby they can apply ERP to manage their day-to-day activities and it will help them design their business processes to equate value for money spent

Semantic discovery and reuse of business process patterns

Patterns currently play an important role in modern information systems (IS) development and their use has mainly been restricted to the design and implementation phases of the development lifecycle. Given the increasing significance of business modelling in IS development, patterns have the potential of providing a viable solution for promoting reusability of recurrent generalized models in the very early stages of development. As a statement of research-in-progress this paper focuses on business process patterns and proposes an initial methodological framework for the discovery and reuse of business process patterns within the IS development lifecycle. The framework borrows ideas from the domain engineering literature and proposes the use of semantics to drive both the discovery of patterns as well as their reuse