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 [...

How to Design More Empathetic Recommender Systems in Social Media

Social media’s value proposition heavily relies on recommender systems suggesting products to buy, events to attend, or people to connect with. These systems currently prioritize user engagement and social media providers’ profit generation over individual users’ well-being. However, making these systems more “empathetic” would benefit social media providers and content creators as users would use social media more often, longer, and increasingly recommend it to other users. By way of a design science research approach, including twelve interviews with system designers, social media experts, psychologists, and users, we develop user-centric design knowledge on making recommender systems in social media more “empathetic.” This design knowledge comprises a conceptual framework, four meta-requirements, and six design principles. It contributes to the research streams “digital responsibility” and “IS for resilience” and provides practical guidance in developing socially responsible recommender systems as next-generation social media services

User Feedback in Controllable and Explainable Social Recommender Systems: a Linguistic Analysis

Controllable and explainable intelligent user interfaces have been used to provide transparent recommendations. Many researchers have explored interfaces that support user control and provide explanations of the recommendation process and models. To extend the works to real-world decision-making scenarios, we need to understand further the users’ mental models of the enhanced system components. In this paper, we make a step in this direction by investigating a free form feedback left by users of social recommender systems to specify the reasons of selecting prompted social recommendations. With a user study involving 50 subjects (N=50), we present the linguistic changes in using controllable and explainable interfaces for a social information-seeking task. Based on our findings, we discuss design implications for controllable and explainable recommender systems

A Social Framework for Set Recommendation in Group Recommender Systems

This research article presents a study about the background in Group Recommender Systems and how social factors are directly related to these applications. Some important group recommender systems in academia are described to exemplify their contribution in different domains. Besides, a framework that is intended to improve group recommender systems is proposed. The main idea of the framework is to enhance social cognition to help the group members agree and make a decision. Its structure includes a process where an influential group is detected among the target groups of people to recommend to. Social influence detection uses the knowledge behind online social connections and interactions. Trying to understand human behavior and ties among groups in a social network and how to use this to improve group recommender systems is considered the main challenge for future research. Combining this with the kind of item recommendation which involves a temporal sequence of ordered elements will present a novel and original path in Group Recommender Systems design. &nbsp

Designing Human-Centered Collective Intelligence

Human-Centered Collective Intelligence (HCCI) is an emergent research area that seeks to bring together major research areas like machine learning, statistical modeling, information retrieval, market research, and software engineering to address challenges pertaining to deriving intelligent insights and solutions through the collaboration of several intelligent sensors, devices and data sources. An archetypal contextual CI scenario might be concerned with deriving affect-driven intelligence through multimodal emotion detection sources in a bid to determine the likability of one movie trailer over another. On the other hand, the key tenets to designing robust and evolutionary software and infrastructure architecture models to address cross-cutting quality concerns is of keen interest in the “Cloud” age of today. Some of the key quality concerns of interest in CI scenarios span the gamut of security and privacy, scalability, performance, fault-tolerance, and reliability. I present recent advances in CI system design with a focus on highlighting optimal solutions for the aforementioned cross-cutting concerns. I also describe a number of design challenges and a framework that I have determined to be critical to designing CI systems. With inspiration from machine learning, computational advertising, ubiquitous computing, and sociable robotics, this literature incorporates theories and concepts from various viewpoints to empower the collective intelligence engine, ZOEI, to discover affective state and emotional intent across multiple mediums. The discerned affective state is used in recommender systems among others to support content personalization. I dive into the design of optimal architectures that allow humans and intelligent systems to work collectively to solve complex problems. I present an evaluation of various studies that leverage the ZOEI framework to design collective intelligence

Layered evaluation of interactive adaptive systems : framework and formative methods

Peer reviewedPostprin

Explanations in Music Recommender Systems in a Mobile Setting

Revised version: some spelling errors corrected.Every day, millions of users utilize their mobile phones to access music streaming services such as Spotify. However, these `black boxes’ seldom provide adequate explanations for their music recommendations. A systematic literature review revealed that there is a strong relationship between moods and music, and that explanations and interface design choices can effect how people perceive recommendations just as much as algorithm accuracy. However, little seems to be known about how to apply user-centric design approaches, which exploit affective information to present explanations, to mobile devices. In order to bridge these gaps, the work of Andjelkovic, Parra, & O’Donovan (2019) was extended upon and applied as non-interactive designs in a mobile setting. Three separate Amazon Mechanical Turk studies asked participants to compare the same three interface designs: baseline, textual, and visual (n=178). Each survey displayed a different playlist with either low, medium, or high music popularity. Results indicate that music familiarity may or may not influence the need for explanations, but explanations are important to users. Both explanatory designs fared equally better than the baseline, and the use of affective information may help systems become more efficient, transparent, trustworthy, and satisfactory. Overall, there does not seem to be a `one design fits all’ solution for explanations in a mobile setting.Master's Thesis in Information ScienceINFO390MASV-INFOMASV-IK

Content Discovery in Online Services: A Case Study on a Video on Demand System

Video-on-demand services have gained popularity in recent years for the large catalogue of content they offer and the ability to watch them at any desired time. Having many options to choose from may be overwhelming for the users and affect negatively the overall experience. The use of recommender systems has been proven to help users discover relevant content faster. However, content discovery is affected not only by the number of choices, but also by the way the content is displayed to the user. Moreover, the development of recommender systems has been commonly focused on increasing their prediction accuracy, rather than the usefulness and user experience. This work takes on a user-centric approach to designing an efficient content discovery experience for its users. The main contribution of this research is a set of guidelines for designing the user interface and recommender system for the aforementioned purpose, formulated based on a user study and existing research. The guidelines were additionally translated into interface designs, which were then evaluated with users. The results showed that users were satisfied with the proposed design and the goal of providing a better content discovery experience was achieved. Moreover, the guidelines were found feasible by the company in which the research was conducted and thus have a high potential to work in a real product. With this research, I aim to highlight the importance of improving the content discovery process both from the perspective of the user interface and a recommender system, and encourage researchers to consider the user experience in those aspects