Design and field evaluation of REMPAD: a recommender system supporting group reminiscence therapy

This paper describes a semi-automated web-based system to facilitate digital remi-niscence therapy for patients with mild-to-moderate dementia, enacted in a group setting. The system, REMPAD, uses proactive recommendation technology to profile participants and groups, and offers interactive multimedia content from the Internet to match these profiles. In this paper, we focus on the design of the system to deliver an innovative personalized group reminiscence experience. We take a user-centered design approach to discover and address the design challenges and considerations. A combination of methodologies is used throughout this research study, including exploratory interviews, prototype use case walkthroughs, and field evaluations. The results of the field evaluation indicate high user satisfaction when using the system, and strong tendency towards repeated use in future. These studies provide an insight into the current practices and challenges of group reminiscence therapy, and inform the design of a multimedia recommender system to support facilitators and group therapy participants

Social Interface and Interaction Design for Group Recommender Systems

Group recommender systems suggest items of interest to a group of people. Traditionally, group recommenders provide recommendations by aggregation the group membersâ preferences. Nowadays, there is a trend of decentralized group recommendation process that leverages the group dynamics and reaches the recommendation goal by allowing group members to influence and persuade each other. So far, the research on group recommender systems mainly focuses on the how to optimize the preference aggregation and enhance the accuracy of recommendations. There is a lack of emphasis on the usersâ social experience, such as interpersonal relationship, emotion exchange, group dynamics, etc. We define the space where user-user interaction occurs in social software as social interfaces. In this thesis, we aim to design and evaluate social interfaces and interactions for group recommender systems. We start with surveying the state-of-the-art of user issues in group recommender systems and interface and interaction design in the broad sense of social applications. We present ten applications and their evaluation via user studies, which lead to a preliminary set of social interface and interaction design guidelines. Based on these guidelines, we develop group recommender systems to investigate the design issues. We then study social interfaces for group recommender systems. We present the design and development ofan experimental platformcalled GroupFun that recommends music to a group of users. We then study the impact of emotion awareness in group recommender systems. More concretely, we design and implement two different methods for emotion awareness: CoFeel and ACTI that visualize emotions using color wheels, and empatheticons that present emotions using dynamic animations of usersâ profile pictures. Our user studies show that emotion awareness tools can help users familiarize with other membersâ preferences, enhance their interpersonal relationships, increase the sense of connectedness in distributed social interactions, and result in higher consensus and satisfaction in group recommendations. We also examine social interactions for persuasive technologies. We design and develop a mobile social game called HealthyTogether that enables dyads to exercise together. With this platform, we study how different social interaction mechanisms, such as social accountability, competition, cooperation, and team spirits, can help usersmotivate and influence each other in physical exercises. We conducted three user studies lasting for up to ten weeks with a total of 80 users. Being accountable for each otherâs performance enhances interpersonal relationships. Supporting users to cooperate on health goals significantly improve their number of steps. When designing competition in the applications, it is crucial to help users to choose comparable buddies. Finally, teamwork in exercises not only helps users to increase their steps, but also help them sustain in exercise. Furthermore, we present an evaluation framework for social persuasive applications. The framework aims at modeling how social strategies and social influence affect user attitudes and behavioral intentions towards the system. Finally, we derive a set of guidelines for social interface and interaction design for group recommender systems. The guidelines can help researchers and practitioners effectively design social experiences for not only group recommenders but also other social software [...

IMPROVING THE DEPENDABILITY OF DESTINATION RECOMMENDATIONS USING INFORMATION ON SOCIAL ASPECTS

Prior knowledge of the social aspects of prospective destinations can be very influential in making travel destination decisions, especially in instances where social concerns do exist about specific destinations. In this paper, we describe the implementation of an ontology-enabled Hybrid Destination Recommender System (HDRS) that leverages an ontological description of five specific social attributes of major Nigerian cities, and hybrid architecture of content-based and case-based filtering techniques to generate personalised top-n destination recommendations. An empirical usability test was conducted on the system, which revealed that the dependability of recommendations from Destination Recommender Systems (DRS) could be improved if the semantic representation of social attributes information of destinations is made a factor in the destination recommendation process

Personalisation and recommender systems in digital libraries

Widespread use of the Internet has resulted in digital libraries that are increasingly used by diverse communities of users for diverse purposes and in which sharing and collaboration have become important social elements. As such libraries become commonplace, as their contents and services become more varied, and as their patrons become more experienced with computer technology, users will expect more sophisticated services from these libraries. A simple search function, normally an integral part of any digital library, increasingly leads to user frustration as user needs become more complex and as the volume of managed information increases. Proactive digital libraries, where the library evolves from being passive and untailored, are seen as offering great potential for addressing and overcoming these issues and include techniques such as personalisation and recommender systems. In this paper, following on from the DELOS/NSF Working Group on Personalisation and Recommender Systems for Digital Libraries, which met and reported during 2003, we present some background material on the scope of personalisation and recommender systems in digital libraries. We then outline the working group’s vision for the evolution of digital libraries and the role that personalisation and recommender systems will play, and we present a series of research challenges and specific recommendations and research priorities for the field

Evaluating the effectiveness of explanations for recommender systems : Methodological issues and empirical studies on the impact of personalization

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