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 [...]

Simulation of nonverbal social interaction and small groups dynamics in virtual environments

Anthony Guye-Vuillème

How can the behaviour of humans who interact with other humans be simulated in virtual environments? This thesis investigates the issue by proposing a number of dedicated models, computer languages, software architectures, and specifications of computational components. It relies on a large knowledge base from the social sciences, which offers concepts, descriptions, and classifications that guided the research process. The simulation of nonverbal social interaction and group dynamics in virtual environments can be divided in two main research problems: (1) an action selection problem, where autonomous agents must be made capable of deciding when, with whom, and how they interact according to individual characteristics of themselves and others; and (2) a behavioural animation problem, where, on the basis of the selected interaction, 3D characters must realistically behave in their virtual environment and communicate nonverbally with others by automatically triggering appropriate actions such as facial expressions, gestures, and postural shifts. In order to introduce the problem of action selection in social environments, a high-level architecture for social agents, based on the sociological concepts of role, norm, and value, is first discussed. A model of action selection for members of small groups, based on proactive and reactive motivational components, is then presented. This model relies on a new tagbased language called Social Identity Markup Language (SIML), allowing the rich specification of agents' social identities and relationships. A complementary model controls the simulation of interpersonal relationship development within small groups. The interactions of these two models create a complex system exhibiting emergent properties for the generation of meaningful sequences of social interactions in the temporal dimension. To address the issues related to the visualization of nonverbal interactions, results are presented of an evaluation experiment aimed at identifying the application requirements through an analysis of how real people interact nonverbally in virtual environments. Based on these results, a number of components for MPEG-4 body animation, AML — a tag-based language for the seamless integration and synchronization of facial animation, body animation, and speech — and a high-level interaction visualization service for the VHD++ platform are described. This service simulates the proxemic and kinesic aspects of nonverbal social interactions, and comprises such functionalities as parametric postures, adapters and observation behaviours, the social avoidance of collisions, intelligent approach behaviours, and the calculation of suitable interaction distances and angles.

EPFL2004

Learning By Collaborative Teaching : An Engaging Multi-Party CoWriter Activity

Thibault Lucien Christian Asselborn, Wafa Monia Benkaouar Johal, Pierre Dillenbourg, Jauwairia Nasir

This paper presents the design of a novel and engaging collaborative learning activity for handwriting where a group of participants simultaneously tutor a Nao robot. This activity was intended to take advantage of both collaborative learning and the learning by teaching paradigm to improve children’s meta-cognition (perception of their own skills). Multiple engagement probes were integrated into the activity as a first step towards fostering long term interactions. As a lot of research targets social interactions, the goal here was to determine whether an engagement strategy focused on the task could be as, or more efficient than one focused on social interactions and participants’ introspection. To that effect, two engagement strategies were implemented. They differed in content but used the same multi-modal design in order to increase participants’ meta-cognitive reflection, once on the task and performances, and once on participants’ enjoyment and emotions. Both strategies were compared to a baseline by probing and assessing engagement at the individual and group level, along the behavioural, emotional and cognitive dimensions, in a between subject experiment with 12 groups of children. The experiments showed that the collaborative task pushed the children to adapt their manner of writing to the group, even though the adopted solution was not always correct. Furthermore, there was no significant difference between the strategies in terms of behaviour on task (behavioural engagement), satisfaction (emotional engagement) or performance (cognitive engagement) as the group dynamics had a stronger impact on the outcome of the collaborative teaching task. Therefore, the task and social engagement strategies can be considered as efficient in the context of collaboration. © 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

2019

Evaluating product search and recommender systems for E-commerce environments

Li Chen, Pratyush Kumar, Pearl Pu Faltings

Online systems that help users select the most preferential item from a large electronic catalog are known as product search and recommender systems. Evaluation of various proposed technologies is essential for further development in this area. This paper describes the design and implementation of two user studies in which a particular product search tool, known as example critiquing, was evaluated against a chosen baseline model. The results confirm that example critiquing significantly reduces users' task time and error rate while increasing decision accuracy. Additionally, the results of the second user study show that a particular implementation of example critiquing also made users more confident about their choices. The main contribution is that through these two user studies, an evaluation framework of three criteria was successfully identified, which can be used for evaluating general product search and recommender systems in E-commerce environments. These two experiments and the actual procedures also shed light on some of the most important issues which need to be considered for evaluating such tools, such as the preparation of materials for evaluation, user task design, the context of evaluation, the criteria, the measures and the methodology of result analyses.