Adapting Recommendation Diversity to Openness to Experience : A Study of Human Behaviour

Postprin

Data centric trust evaluation and prediction framework for IOT

© 2017 ITU. Application of trust principals in internet of things (IoT) has allowed to provide more trustworthy services among the corresponding stakeholders. The most common method of assessing trust in IoT applications is to estimate trust level of the end entities (entity-centric) relative to the trustor. In these systems, trust level of the data is assumed to be the same as the trust level of the data source. However, most of the IoT based systems are data centric and operate in dynamic environments, which need immediate actions without waiting for a trust report from end entities. We address this challenge by extending our previous proposals on trust establishment for entities based on their reputation, experience and knowledge, to trust estimation of data items [1-3]. First, we present a hybrid trust framework for evaluating both data trust and entity trust, which will be enhanced as a standardization for future data driven society. The modules including data trust metric extraction, data trust aggregation, evaluation and prediction are elaborated inside the proposed framework. Finally, a possible design model is described to implement the proposed ideas

The Convergence of Digital-Libraries and the Peer-Review Process

Pre-print repositories have seen a significant increase in use over the past fifteen years across multiple research domains. Researchers are beginning to develop applications capable of using these repositories to assist the scientific community above and beyond the pure dissemination of information. The contribution set forth by this paper emphasizes a deconstructed publication model in which the peer-review process is mediated by an OAI-PMH peer-review service. This peer-review service uses a social-network algorithm to determine potential reviewers for a submitted manuscript and for weighting the relative influence of each participating reviewer's evaluations. This paper also suggests a set of peer-review specific metadata tags that can accompany a pre-print's existing metadata record. The combinations of these contributions provide a unique repository-centric peer-review model that fits within the widely deployed OAI-PMH framework.Comment: Journal of Information Science [in press

Understanding the Role of Interactivity and Explanation in Adaptive Experiences

Adaptive experiences have been an active area of research in the past few decades, accompanied by advances in technology such as machine learning and artificial intelligence. Whether the currently ongoing research on adaptive experiences has focused on personalization algorithms, explainability, user engagement, or privacy and security, there is growing interest and resources in developing and improving these research focuses. Even though the research on adaptive experiences has been dynamic and rapidly evolving, achieving a high level of user engagement in adaptive experiences remains a challenge. %????? This dissertation aims to uncover ways to engage users in adaptive experiences by incorporating interactivity and explanation through four studies. Study I takes the first step to link the explanation and interactivity in machine learning systems to facilitate users\u27 engagement with the underlying machine learning model with the Tic-Tac-Toe game as a use case. The results show that explainable machine learning (XML) systems (and arguably XAI systems in general) indeed benefit from mechanisms that allow users to interact with the system\u27s internal decision rules. Study II, III, and IV further focus on adaptive experiences in recommender systems in specific, exploring the role of interactivity and explanation to keep the user “in-the-loop” in recommender systems, trying to mitigate the ``filter bubble\u27\u27 problem and help users in self-actualizing by supporting them in exploring and understanding their unique tastes. Study II investigates the effect of recommendation source (a human expert vs. an AI algorithm) and justification method (needs-based vs. interest-based justification) on professional development recommendations in a scenario-based study setting. The results show an interaction effect between these two system aspects: users who are told that the recommendations are based on their interests have a better experience when the recommendations are presented as originating from an AI algorithm, while users who are told that the recommendations are based on their needs have a better experience when the recommendations are presented as originating from a human expert. This work implies that while building the proposed novel movie recommender system covered in study IV, it would provide a better user experience if the movie recommendations are presented as originating from algorithms rather than from a human expert considering that movie preferences (which will be visualized by the movies\u27 emotion feature) are usually based on users\u27 interest. Study III explores the effects of four novel alternative recommendation lists on participants’ perceptions of recommendations and their satisfaction with the system. The four novel alternative recommendation lists (RSSA features) which have the potential to go beyond the traditional top N recommendations provide transparency from a different level --- how much else does the system learn about users beyond the traditional top N recommendations, which in turn enable users to interact with these alternative lists by rating the initial recommendations so as to correct or confirm the system\u27s estimates of the alternative recommendations. The subjective evaluation and behavioral analysis demonstrate that the proposed RSSA features had a significant effect on the user experience, surprisingly, two of the four RSSA features (the controversial and hate features) perform worse than the traditional top-N recommendations on the measured subjective dependent variables while the other two RSSA features (the hipster and no clue items) perform equally well and even slightly better than the traditional top-N (but this effect is not statistically significant). Moreover, the results indicate that individual differences, such as the need for novelty and domain knowledge, play a significant role in users’ perception of and interaction with the system. Study IV further combines diversification, visualization, and interactivity, aiming to encourage users to be more engaged with the system. The results show that introducing emotion as an item feature into recommender systems does help in personalization and individual taste exploration; these benefits are greatly optimized through the mechanisms that diversify recommendations by emotional signature, visualize recommendations on the emotional signature, and allow users to directly interact with the system by tweaking their tastes, which further contributes to both user experience and self-actualization. This work has practical implications for designing adaptive experiences. Explanation solutions in adaptive experiences might not always lead to a positive user experience, it highly depends on the application domain and the context (as studied in all four studies); it is essential to carefully investigate a specific explanation solution in combination with other design elements in different fields. Introducing control by allowing for direct interactivity (vs. indirect interactivity) in adaptive systems and providing feedback to users\u27 input by integrating their input into the algorithms would create a more engaging and interactive user experience (as studied in Study I and IV). And cumulatively, appropriate direct interaction with the system along with deliberate and thoughtful designs of explanation (including visualization design with the application environment fully considered), which are able to arouse user reflection or resonance, would potentially promote both user experience and user self-actualization

Examining the trip experience on competitive advantage creation in tourism

Researchers thanked for the support by Directorate of Research and Community Service. Directorate General of Strengthening Research and Development. Ministry of Research, Technology, and Higher Education of Indonesia for the funding in year 2019.Purpose: This study aims to examine the trip experience on competitive advantage creation in Indonesia tourism. Trip experience includes transportation, accommodation, tour guide, and tourism activities. Design/Methodology/Approach: The research population consists of all international tourists who visited Indonesia through Soekarno Hatta International Airport (Jakarta) and Ngurah Rai International Airport (Bali). The questionnaire was constructed in English and translated into Arabic and Mandarin. Convenience sampling techniques were used to collect data. Out of 500 questionnaires distributed, only 415 were valid and used for analysis. Partial Least Square by SMART-PLS 3.0 software was used for data analysis. Findings: Result shows that transportation, accommodation and tourist activities are positively significant factors on tourist loyalty and mediated by tourist satisfaction. Whereas, tour guides have positively but not significant influence on tourist loyalty and mediated by tourist satisfaction. Practical implications: The study provides a practical implication which requires a close relationship of state governments and tourism companies in order to have good cooperative and coordination to competitive advantage creation. Originality/Value: As this study is based on recent studies on tourism destination in competitive advantage creation it brings a new insight of analyzing the two cities of highest tourists visits in order to understand the condition of overall Indonesian tourism.peer-reviewe

Putting Community First: A Promising Approach to Federal Collaboration for Environmental Improvement: An Evaluation of the Community Action for a Renewed Environment (CARE) Demonstration Program

This report is an independent evaluation of the Environmental Protection Agency's (EPA) Community Action for a Renewed Environment (CARE) Demonstration Program, a community-driven process that uses the best available data to help communities set priorities and take action on their greatest environmental risks. CARE fosters local partnerships that seek participation from business, government, organizations, residents and EPA staff. It also supports a public, transparent planning and implementation process based on collaborative decision-making and shared action.Key FindingsThe National Academy Panel overseeing this effort was impressed by the dedication of the EPA staff to this unique initiative and commended the EPA for its efforts to partner with communities in achieving important long-term and sustainable environmental improvements at the local level. Recommended actions for the CARE Program include: (1) develop and implement a multifaceted information sharing approach; (2) coordinate and refine internal program management activities; and (3) develop a strategic plan and a business plan for CARE