Multi-modal Embedding Fusion-based Recommender

Recommendation systems have lately been popularized globally, with primary use cases in online interaction systems, with significant focus on e-commerce platforms. We have developed a machine learning-based recommendation platform, which can be easily applied to almost any items and/or actions domain. Contrary to existing recommendation systems, our platform supports multiple types of interaction data with multiple modalities of metadata natively. This is achieved through multi-modal fusion of various data representations. We deployed the platform into multiple e-commerce stores of different kinds, e.g. food and beverages, shoes, fashion items, telecom operators. Here, we present our system, its flexibility and performance. We also show benchmark results on open datasets, that significantly outperform state-of-the-art prior work.Comment: 7 pages, 8 figure

A qualitative study of stakeholders' perspectives on the social network service environment

Over two billion people are using the Internet at present, assisted by the mediating activities of software agents which deal with the diversity and complexity of information. There are, however, ethical issues due to the monitoring-and-surveillance, data mining and autonomous nature of software agents. Considering the context, this study aims to comprehend stakeholders' perspectives on the social network service environment in order to identify the main considerations for the design of software agents in social network services in the near future. Twenty-one stakeholders, belonging to three key stakeholder groups, were recruited using a purposive sampling strategy for unstandardised semi-structured e-mail interviews. The interview data were analysed using a qualitative content analysis method. It was possible to identify three main considerations for the design of software agents in social network services, which were classified into the following categories: comprehensive understanding of users' perception of privacy, user type recognition algorithms for software agent development and existing software agents enhancement

Knowledge-mapping of blockchain technology applications for a banking institution

Abstract : Blockchain technology is a relatively new technology which provides many opportunities for knowledge-mapping. Blockchain technology is best described as a decentralised ledger system that stores information about transactions and uses digital currencies such as Bitcoin. The best possible utilisation of a new technology usually depends on how quickly people can develop and apply new knowledge of the technology. Knowledge is a key component to leverage the most useful features of any new technology. Moreover, it is crucial to know how to avoid the pitfalls of a new technology in order to develop solutions. This study’s unit of analysis is knowledge of blockchain technology, that is, the knowledge possessed by people operating in the banking industry. The banking industry is sternly regulated in all jurisdictions and employee know-how is a valuable resource. The recent wide dissemination of blockchain technology, the popularity of cryptocurrencies, and the Initial Coin Offering have contributed to the fact that financial institutions’ management underline the vast potential of blockchain technology in the financial industry. For example, large banks are conducting tests of decentralised asset technology and implementing decentralised ledger systems in business processes. Banks are investing in projects and start-ups that are developing blockchain-based solutions. Therefore, bank employees with know-how and prior experience with blockchain are essential to create blockchain solutions. The objective of this study is to map the existing know-how and identify knowledge gaps of blockchain technology know-how and its possible application in a South African Banking Institution (SABI). This is done through an analysis of knowledge of how the utilisation of blockchain technology changes the existing operations models of financial institutions. The research methodology consists of an inductive knowledge-mapping strategy and mixed-method approach. The quantitative data collection method involved gathering data via an online questionnaire sent to a purposive sample, namely, SABI’s clients, investors, experts, and individuals with the common denominator: Blockchain technology knowledge interest who had attended the Blockchain Africa Conference. The qualitative data collection method was an interview with individuals who had a specific technical knowledge of blockchain technology, with the common denominator: SABI blockchain knowledge group. iv The data analysis was sequential; the quantitative data analysis was followed by qualitative data analysis. The findings identify categories of knowledge that are needed to inform and build new blockchain technology-based operations models. Knowledge gaps were identified in the SABI. Based on the findings, the study conceptualises a knowledge map and develops a theory, namely: If the blockchain knowledge maps of financial institutions integrate knowledge across their Core Banking Application pillars, then the financial services industry will create an Internet of Value-Exchange advantage for everyone on the network. Further study is required in order to test this theory. A key recommendation is to perform knowledge-mapping of the Core Banking Application pillars as the next step of SABI’s knowledge maturity of blockchain technology. In conclusion, knowledge maturity of blockchain technology is essential to create an Internet of Value-Exchange advantage for everyone within the network. The mapping of knowledge provides a measurement of knowledge maturity. Blockchain technology provides many opportunities for knowledge-mapping.M.Phil. (Information Management