An integrated mobile content recommendation system

Many features have been added to mobile devices to assist the user's information consumption. However, there are limitations due to information overload on the devices, hardware usability and capacity. As a result, content filtering in a mobile recommendation system plays a vital role in the solution to this problem. A system that utilises content filtering can recommend content which matches a user's needs based on user preferences with a higher accuracy rate. However, mobile content recommendation systems have problems and limitations related to cold start and sparsity. The problems can be viewed as first time connection and first content rating for non-interactive recommendation systems where information is insufficient to predict mobile content which will match with a user's needs. In addition, how to find relevant items for the content recommendation system which are related to a user's profile is also a concern. An integrated model that combines the user group identification and mobile content filtering for mobile content recommendation was proposed in this study in order to address the current limitations of the mobile content recommendation system. The model enhances the system by finding the relevant content items that match with a user's needs based on the user's profile. A prototype of the client-side user profile modelling is also developed to demonstrate the concept. The integrated model applies clustering techniques to determine groups of users. The content filtering implemented classification techniques to predict the top content items. After that, an adaptive association rules technique was performed to find relevant content items. These approaches can help to build the integrated model. Experimental results have demonstrated that the proposed integrated model performs better than the comparable techniques such as association rules and collaborative filtering. These techniques have been used in several recommendation systems. The integrated model performed better in terms of finding relevant content items which obtained higher accuracy rate of content prediction and predicted successful recommended relevant content measured by recommendation metrics. The model also performed better in terms of rules generation and content recommendation generation. Verification of the proposed model was based on real world practical data. A prototype mobile content recommendation system with client-side user profile has been developed to handle the revisiting user issue. In addition, context information, such as time-of-day and time-of-week, could also be used to enhance the system by recommending the related content to users during different time periods. Finally, it was shown that the proposed method implemented fewer rules to generate recommendation for mobile content users and it took less processing time. This seems to overcome the problems of first time connection and first content rating for non-interactive recommendation systems

Proposal of a web site engagement scale and research model. Analysis of the influence of intra web site comparative behaviour

A Web site engagement scale is suggested that serves as the basis of a two part-model. The first part studies the influence of the online comparative behaviour of consumers on Web site engagement using data obtained from respondents that selected a holiday package on an online travel agency capable of remotely tracing and recording their intra-Web page and intra-Web site behaviour. The second part of the model studies the influence of Web site engagement on consequences highly relevant for online marketers. The results confirm that the Web site engagement construct has five dimensions: positive affect, focused attention, challenge, curiosity and involvement. Likewise antecedents and consequences of Web site engagement are confirmed. The model is estimated with partial least squares path modelling (PLSPM).En esta investigación se propone una escala de “enganche con sitios Web” que sirve de base para un modelo con dos partes. En la primera parte se estudia la influencia del comportamiento comparativo online de consumidores utilizando datos obtenidos a partir de encuestados que escogieron un paquete vacacional en una agencia de viajes online capaz de registrar remotamente el comportamiento intra-página Web e intra-sitio Web. En la segunda parte del modelo se estudia la influencia del constructo enganche con sitios Web sobre consecuencias de relevancia para online marketers. Los resultados confirman que el constructo enganche con sitios Web tiene cinco dimensiones: afecto positivo, atención centrada, curiosidad, implicación y reto. Asimismo se confirman antecedentes y consecuencias de este constructo. La metodología de estimación se basa sobre modelización estructural con partial least squares path modelling (PLSPM)

Modelling Web Usage in a Changing Environment

Eiben, A.E. [Promotor]Kowalczyk, W. [Copromotor

Exploring News Environment, Interface Usage, and Gratifications in Real-time Internet News Browsing

Master'sMASTER OF ART

Can Upward Brand Extensions be an Opportunity for Marketing Managers During the Covid-19 Pandemic and Beyond?

Early COVID-19 research has guided current managerial practice by introducing more products across different product categories as consumers tried to avoid perceived health risks from food shortages, i.e. horizontal brand extensions. For example, Leon, a fast-food restaurant in the UK, introduced a new range of ready meal products. However, when the food supply stabilised, availability may no longer be a concern for consumers. Instead, job losses could be a driver of higher perceived financial risks. Meanwhile, it remains unknown whether the perceived health or financial risks play a more significant role on consumers’ consumptions. Our preliminary survey shows perceived health risks outperform perceived financial risks to positively influence purchase intention during COVID-19. We suggest such a result indicates an opportunity for marketers to consider introducing premium priced products, i.e. upward brand extensions. The risk-as�feelings and signalling theories were used to explain consumer choice under risk may adopt affective heuristic processing, using minimal cognitive efforts to evaluate products. Based on this, consumers are likely to be affected by the salient high-quality and reliable product cue of upward extension signalled by its premium price level, which may attract consumers to purchase when they have high perceived health risks associated with COVID-19. Addressing this, a series of experimental studies confirm that upward brand extensions (versus normal new product introductions) can positively moderate the positive effect between perceived health risks associated with COVID-19 and purchase intention. Such an effect can be mediated by affective heuristic information processing. The results contribute to emergent COVID-19 literature and managerial practice during the pandemic but could also inform post-pandemic thinking around vertical brand extensions

The Big Five:Addressing Recurrent Multimodal Learning Data Challenges

The analysis of multimodal data in learning is a growing field of research, which has led to the development of different analytics solutions. However, there is no standardised approach to handle multimodal data. In this paper, we describe and outline a solution for five recurrent challenges in the analysis of multimodal data: the data collection, storing, annotation, processing and exploitation. For each of these challenges, we envision possible solutions. The prototypes for some of the proposed solutions will be discussed during the Multimodal Challenge of the fourth Learning Analytics & Knowledge Hackathon, a two-day hands-on workshop in which the authors will open up the prototypes for trials, validation and feedback

Multimodal Challenge: Analytics Beyond User-computer Interaction Data

This contribution describes one the challenges explored in the Fourth LAK Hackathon. This challenge aims at shifting the focus from learning situations which can be easily traced through user-computer interactions data and concentrate more on user-world interactions events, typical of co-located and practice-based learning experiences. This mission, pursued by the multimodal learning analytics (MMLA) community, seeks to bridge the gap between digital and physical learning spaces. The “multimodal” approach consists in combining learners’ motoric actions with physiological responses and data about the learning contexts. These data can be collected through multiple wearable sensors and Internet of Things (IoT) devices. This Hackathon table will confront with three main challenges arising from the analysis and valorisation of multimodal datasets: 1) the data collection and storing, 2) the data annotation, 3) the data processing and exploitation. Some research questions which will be considered in this Hackathon challenge are the following: how to process the raw sensor data streams and extract relevant features? which data mining and machine learning techniques can be applied? how can we compare two action recordings? How to combine sensor data with Experience API (xAPI)? what are meaningful visualisations for these data

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

The student-produced electronic portfolio in craft education

The authors studied primary school students’ experiences of using an electronic portfolio in their craft education over four years. A stimulated recall interview was applied to collect user experiences and qualitative content analysis to analyse the collected data. The results indicate that the electronic portfolio was experienced as a multipurpose tool to support learning. It makes the learning process visible and in that way helps focus on and improves the quality of learning. © ISLS.Peer reviewe