Design principles for competence-based recommender systems

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A Theory-Driven Design Framework for Social Recommender Systems

Social recommender systems utilize data regarding users’ social relationships in filtering relevant information to users. To date, results show that incorporating social relationship data – beyond consumption profile similarity – is beneficial only in a very limited set of cases. The main conjecture of this study is that the inconclusive results are, at least to some extent, due to an under-specification of the nature of the social relations. To date, there exist no clear guidelines for using behavioral theory to guide systems design. Our primary objective is to propose a methodology for theory-driven design. We enhance Walls et al.’s (1992) IS Design Theory by introducing the notion of “applied behavioral theory,” as a means of better linking theory and system design. Our second objective is to apply our theory-driven design methodology to social recommender systems, with the aim of improving prediction accuracy. A behavioral study found that some social relationships (e.g., competence, benevolence) are most likely to affect a recipient’s advice-taking decision. We designed, developed, and tested a recommender system based on these principles, and found that the same types of relationships yield the best recommendation accuracy. This striking correspondence highlights the importance of behavioral theory in guiding system design. We discuss implications for design science and for research on recommender systems

The Mediation Effect of Trusting Beliefs on the Relationship Between Expectation-Confirmation and Satisfaction with the Usage of Online Product Recommendation

Online Product Recommendations (OPRs) are increasingly available to onlinecustomers as a value-added self-service in evaluating and choosing a product.Research has highlighted several advantages that customers can gain from usingOPRs. However, the realization of these advantages depends on whether and towhat extent customers embrace and fully utilise them. The relatively low OPR USAgerate indicates that customers have not yet developed trust in OPRs’ performance.Past studies also have established that satisfaction is a valid measure of systemperformance and a consistent significant determinant of users’ continuous systemusage. Therefore, this study aimed to examine the mediation effect of trustingbeliefs on the relationship between expectation-confirmation and satisfaction. Theproposed research model is tested using data collected via an online survey from626 existing users of OPRs. The empirical results revealed that social-psychologicalbeliefs (perceived confirmation and trust) are significant contributors to customersatisfaction with OPRs. Additionally, trusting beliefs partially mediate the impactof perceived confirmation on customer satisfaction. Moreover, this study validatesthe extensions of the interpersonal trust construct to trust in OPRs and examinesthe nomological validity of trust in terms of competence, benevolence, andintegrity. The findings provide a number of theoretical and practical implications.&nbsp

Knowledge modelling with the open source tool myCBR

Building knowledge intensive Case-Based Reasoning applications requires tools that support this on-going process between domain experts and knowledge engineers. In this paper we will introduce how the open source tool myCBR 3 allows for flexible knowledge elicitation and formalisation form CBR and non CBR experts. We detail on myCBR 3 's versatile approach to similarity modelling and will give an overview of the Knowledge Engineering workbench, providing the tools for the modelling process. We underline our presentation with three case studies of knowledge modelling for technical diagnosis and recommendation systems using myCBR 3

Privacy, security, and trust issues in smart environments

Recent advances in networking, handheld computing and sensor technologies have driven forward research towards the realisation of Mark Weiser's dream of calm and ubiquitous computing (variously called pervasive computing, ambient computing, active spaces, the disappearing computer or context-aware computing). In turn, this has led to the emergence of smart environments as one significant facet of research in this domain. A smart environment, or space, is a region of the real world that is extensively equipped with sensors, actuators and computing components [1]. In effect the smart space becomes a part of a larger information system: with all actions within the space potentially affecting the underlying computer applications, which may themselves affect the space through the actuators. Such smart environments have tremendous potential within many application areas to improve the utility of a space. Consider the potential offered by a smart environment that prolongs the time an elderly or infirm person can live an independent life or the potential offered by a smart environment that supports vicarious learning

Tell Me Why (I Want It That Way) – Effects of Explanations and Online Customer Reviews on Trust in Recommender Systems

Review-based recommender systems (RS) have shown great potential in helping users manage information overload and find suitable items. However, a lack of trust still impedes the widespread acceptance of RS. To increase users’ trust, research proposes various methods to generate justifications or explanations. Furthermore, online customer reviews (OCRs) are found to be a trustworthy and reliable source of information. However, it is still unclear how justifications compare to explanations in their influence on users’ trust and whether basing them on OCRs additionally adds trust. Hence, we conduct an online experiment with 531 participants and find that explanations exceed justifications in increasing users’ trust, while basing them on OCRs directly increases users’ intentions to use the system and adopt recommendations without increasing trust in the RS themselves. Unifying different research streams from review-based RS and Explainable Artificial Intelligence, we provide an overarching, holistic view on the conception of justifications and explanations