Recommender Systems for Personalized Gamification

© Owners/Authors, 2017. This is the author's version of the work. It is posted here for your personal use. Not for redistribution. The definitive Version of Record was published in UMAP '17 - Adjunct Publication of the 25th Conference on User Modeling, Adaptation and Personalization.DOI: 10.1145/3099023.3099114Gamification has been used in a variety of application domains to promote behaviour change. Nevertheless, the mechanisms behind it are still not fully understood. Recent empirical results have shown that personalized approaches can potentially achieve better results than generic approaches. However, we still lack a general framework for building personalized gameful applications. To address this gap, we present a novel general framework for personalized gameful applications using recommender systems (i.e., software tools and technologies to recommend suggestions to users that they might enjoy). This framework contributes to understanding and building effective persuasive and gameful applications by describing the different building blocks of a recommender system (users, items, and transactions) in a personalized gamification context.NSERC SSHRC Mitacs CNPq, Brazi

Modelling the recommendation technique for achieving awareness in serious game for obesity

Obesity among children is increasing alarmingly. Therefore it is crucial to instill the awareness about the danger of being obese to the children. To instill the awareness is a challenge. A good approach is required for the message to be accepted and conceptualized by the children. Serious game has been an important mechanism to assist the achievement of many serious purposes other than for entertainment and enjoyment. This paper proposes the implementation of content-based recommendation technique in serious game for obesity awareness among children. Several recommendation techniques for awareness had been studied and the content-based recommendation technique is found to be suitable to be implemented in games. The game was developed according to game development approach and fulfilled the game characteristics. For this preliminary study, 10 children participated in the experiment to determine the effectiveness of the game. The participants were given a pre-and post test to answer before and after playing the game. The result of the test shows that participants achieved the obesity awareness after playing the game. The content-based recommendation technique is applicable to be adopted in serious games to instill awareness in the players

HeyMoe: Leveraging virtual robot and game mechanics in mobile prototype application design for Chinese language learning

With the growth of internet technology, individuals currently have a better experience in using interactive systems like desktop computer software or mobile device applications than ever before. Moreover, using mobile phones in learning activities is becoming increasingly popular. However, the learning applications today do not fully meet the learner’s needs, especially for young adults who have some interest but lack fundamental knowledge in their study subjects. Combining mobile phone technology and game elements could make it possible to solve this problem. In this way, the limitations of location and the sometimes boring nature of the learning activities could be efficiently reduced. This study presents an idea of applying virtual robot and game elements to language learning activities and creating a mobile device application prototype to enhance the learner’s Chinese study experience

Recommendation Systems: A Systematic Review

This article presents a comprehensive and objective systematic review of existing research on recommendation systems with regards to core theory, latest studies, various applications, current attitudes, and potential future applications. The research is mainly based on exploring professional peer-reviewed studies and articles and using their abstracts to create a comprehensive and unbiased review of existing research. The following search terms were used to identify articles and studies for the research: recommendation systems; recommender systems; core theory of recommender systems; current attitudes towards recommendation systems; latest studies on recommendation systems; applications of recommendation systems; potential studies on recommendation systems; and future potential applications of recommendation systems. The research also used the advanced search filter to locate recent studies for comparison by limiting the search by year to find studies published from 2021 onwards. Most literature on this area highlights the importance of recommendation systems in almost all aspects of modern life. Specifically, recommendation systems have become critical components in business, health care, education, marketing, and social networking domains. Additionally, most studies identified reinforcement of learning and deep learning techniques as significant developments in the field. These techniques form the backbone of most modern recommendation systems. The primary concern that could hinder further evolution systems is their consequent filter bubble effects which many studies showed to be problematic. Healthcare is a central area that shows tremendous potential for these systems. Although recommender systems have been implemented in this domain, there remains a lot of untapped potential that, if unleashed, could revolutionize medicine and healthcare. But the problems facing these systems have to be tackled first to establish trust. Keywords: Recommendation systems, Recommender systems, Deep learning, Reinforcement learning DOI: 10.7176/CEIS/13-4-04 Publication date:August 31st 202

A Gamification Engine Architecture for Enhancing Behavioral Change Support Systems

This paper presents a gamified framework designed to offer behavioural change support and treatment adherence services to people living with Dementia (PLWD), their caregivers and medical/social professionals

Motivated Agents : Toward the Computational Modeling of Motivational Affordances

Video games routinely use procedural content generation, player modelling, and other forms of computational interaction that provide a good starting point for engaging computational interfaces. However, across these practices, games model environment (game content) and actor (player type) separately, which is out of tune with both basic and applied research. The ecological construct of motivational affordances, formalized as actor-environment system ratios, provides a promising alternative that could also prove fruitful for computational interaction in general

Strategic corporate communication in the digital age

This chapter presents a systematic review of over thirty (30) types of online marketing methods. It describes different methods like email marketing, social network marketing, in-game marketing and augmented reality marketing, among other approaches. The researchers discuss that the rationale for using these online marketing strategies is to increase brand awareness, customer centric marketing and consumer loyalty. They shed light on various personalization methods including recommendation systems and user generated content in their taxonomy of online marketing terms. Hence, they explain how these online marketing methods are related to each other. The researchers contend that the boundaries between online marketing methods have not been clarified enough within the academic literature. Therefore, this chapter provides a better understanding of different online marketing methods. A review of the literature suggests that the ‘oldest’ online marketing methods including the email and the websites are still very relevant for today’s corporate communication. In conclusion, the researchers put forward their recommendations for future research about contemporary online marketing methods.peer-reviewe

Modeling tourists' personality in recommender systems: how does personality influence preferences for tourist attractions?

Personalization is increasingly being perceived as an important factor for the effectiveness of Recommender Systems (RS). This is especially true in the tourism domain, where travelling comprises emotionally charged experiences, and therefore, the more about the tourist is known, better recommendations can be made. The inclusion of psychological aspects to generate recommendations, such as personality, is a growing trend in RS and they are being studied to provide more personalized approaches. However, although many studies on the psychology of tourism exist, studies on the prediction of tourist preferences based on their personality are limited. Therefore, we undertook a large-scale study in order to determine how the Big Five personality dimensions influence tourists' preferences for tourist attractions, gathering data from an online questionnaire, sent to Portuguese individuals from the academic sector and their respective relatives/friends (n=508). Using Exploratory and Confirmatory Factor Analysis, we extracted 11 main categories of tourist attractions and analyzed which personality dimensions were predictors (or not) of preferences for those tourist attractions. As a result, we propose the first model that relates the five personality dimensions with preferences for tourist attractions, which intends to offer a base for researchers of RS for tourism to automatically model tourist preferences based on their personality.GrouPlanner Project under the European Regional Development Fund POCI-01-0145-FEDER29178 and by National Funds through the FCT – Fundação para a Ciência e a Tecnologia (Portuguese Foundation for Science and Technology) within the Projects UIDB/00319/2020 and UIDB/00760/202