Entertainment Personalization Mechanism through Cross-Domain User Modeling

Abstract. The growth of available entertainment information services, such as movies and CD listings, or travels and recreational activities, raises a need for personalization techniques for filtering and adapting contents to customer's interest and needs. Personalization technologies rely on users data, represented as User Models (UMs). UMs built by specific services are usually not transferable due to commercial competition and models ' representation heterogeneity. This paper focuses on the second obstacle and discusses architecture for mediating UMs across different domains of entertainment. The mediation facilitates improving the accuracy of the UMs and upgrading the provided personalization. 1

Considering temporal aspects in recommender systems: a survey

Under embargo until: 2023-07-04The widespread use of temporal aspects in user modeling indicates their importance, and their consideration showed to be highly effective in various domains related to user modeling, especially in recommender systems. Still, past and ongoing research, spread over several decades, provided multiple ad-hoc solutions, but no common understanding of the issue. There is no standardization and there is often little commonality in considering temporal aspects in different applications. This may ultimately lead to the problem that application developers define ad-hoc solutions for their problems at hand, sometimes missing or neglecting aspects that proved to be effective in similar cases. Therefore, a comprehensive survey of the consideration of temporal aspects in recommender systems is required. In this work, we provide an overview of various time-related aspects, categorize existing research, present a temporal abstraction and point to gaps that require future research. We anticipate this survey will become a reference point for researchers and practitioners alike when considering the potential application of temporal aspects in their personalized applications.acceptedVersio

Layered evaluation of interactive adaptive systems : framework and formative methods

Peer reviewedPostprin

Social media advertising: The role of personalization in Performance expectancy and customer purchase intention

Dissertation presented as the partial requirement for obtaining a Master's degree in Statistics and Information Management, specialization in Marketing Research and CRMSocial media is being widely used as a platform to manage marketing and advertising activities. Organizations are always having a challenge in how they can design social media advertising by spending a lot of time, money, and resources on social media ads to successfully fascinate customers and motivate them to purchase their brands. This study aims to evaluate the impact of personalization on purchase intention and also investigating the role of personalization moderating the relation between Performance expectancy and customer purchase intention. The conceptual model will be based on the extending Unified Theory of Acceptance and Use of Technology (UTAUT2). This study will be expected to provide several theoretical and practical rules on how personalization can successfully impact customer purchase intention over social media platforms

Designing Human-Centered Collective Intelligence

Human-Centered Collective Intelligence (HCCI) is an emergent research area that seeks to bring together major research areas like machine learning, statistical modeling, information retrieval, market research, and software engineering to address challenges pertaining to deriving intelligent insights and solutions through the collaboration of several intelligent sensors, devices and data sources. An archetypal contextual CI scenario might be concerned with deriving affect-driven intelligence through multimodal emotion detection sources in a bid to determine the likability of one movie trailer over another. On the other hand, the key tenets to designing robust and evolutionary software and infrastructure architecture models to address cross-cutting quality concerns is of keen interest in the “Cloud” age of today. Some of the key quality concerns of interest in CI scenarios span the gamut of security and privacy, scalability, performance, fault-tolerance, and reliability. I present recent advances in CI system design with a focus on highlighting optimal solutions for the aforementioned cross-cutting concerns. I also describe a number of design challenges and a framework that I have determined to be critical to designing CI systems. With inspiration from machine learning, computational advertising, ubiquitous computing, and sociable robotics, this literature incorporates theories and concepts from various viewpoints to empower the collective intelligence engine, ZOEI, to discover affective state and emotional intent across multiple mediums. The discerned affective state is used in recommender systems among others to support content personalization. I dive into the design of optimal architectures that allow humans and intelligent systems to work collectively to solve complex problems. I present an evaluation of various studies that leverage the ZOEI framework to design collective intelligence

Integrating serious games in adaptive hypermedia applications for personalised learning experiences

Game-based approaches to learning are increasingly recognized for their potential to stimulate intrinsic motivation amongst learners. While a range of examples of effective serious games exist, creating high-fidelity content with which to populate games is resource-intensive task. To reduce this resource requirement, research is increasingly exploring means to reuse and repurpose existing games. Education has proven a popular application area for Adaptive Hypermedia (AH), as adaptation can offer enriched learning experiences. Whilst content has mainly been in the form of rich text, various efforts have been made to integrate serious games into AH. However, there is little in the way of effective integrated authoring and user modeling support. This paper explores avenues for effectively integrating serious games into AH. In particular, we consider authoring and user modeling aspects in addition to integration into run-time adaptation engines, thereby enabling authors to create AH that includes an adaptive game, thus going beyond mere selection of a suitable game and towards an approach with the capability to adapt and respond to the needs of learners and educators

Mobile Advertising Technology Acceptance Model (M-TAM): An Extension of TAM in Mobile Marketing Context

Mobile advertising technology has revolutionized the way companies interact with their customers. This has offered both marketers and consumers the advantages of convenience, flexibility, and ubiquity etc. Irrespective of the advantages of mobile advertising technology, it is not easy for marketers and researchers to form adoptable mobile advertising technology for consumers without being in a position to precisely foresee consumers’ behavioural outcome. Additionally, this field appears to be scant in academic researches. The current study was initiated to empirically examine the influence of m-advertising technological factors (i.e., ease of use, usefulness, personalization and credibility) on consumers’ adoption behaviour. These factors were also mediated by emotional value in order to influence end users’ adoption behaviour. A quantitative approach based upon a cross-sectional administered survey was employed to collect the data from university going student as respondents. The Partial Least Square (PLS) two step path modelling was used through SmartPLS 3 to test the direct and indirect hypotheses. The results indicate that mobile advertising technology factors (i.e., ease of use, usefulness, personalization and credibility) directly and indirectly impact consumers’ adoption behaviour through emotional value. Based on the findings of the study, theoretical and practical contributions along with certain limitations and future researches recommendation are discussed consequentially