We identify shortcomings of current recommender systems.We present an interactive recommender framework to tackle the shortcomings.We analyze existing interactive recommenders along the dimensions of our framework.Based on the analysis, we identify future research challenges and opportunities. Recommender systems have been researched extensively over the past decades. Whereas several algorithms have been developed and deployed in various application domains, recent research efforts are increasingly oriented towards the user experience of recommender systems. This research goes beyond accuracy of recommendation algorithms and focuses on various human factors that affect acceptance of recommendations, such as user satisfaction, trust, transparency and sense of control. In this paper, we present an interactive visualization framework that combines recommendation with visualization techniques to support human-recommender interaction. Then, we analyze existing interactive recommender systems along the dimensions of our framework, including our work. Based on our survey results, we present future research challenges and opportunities.

Interactive recommender systems

On behalf of the Technical Program Committee, welcome to the CHI 2005 conference DVD! The Extended Abstracts portion of this disc includes submissions from all conference venues except Papers (which you can find in the Conference Proceedings section). Here you will find a formal record of the discussions, demonstrations, and debates that will occur during the conference. Highlights include:
short papers from the Late-Breaking Results venue,
videos of demonstrations from our new Interactivity Chamber,
case studies in innovation and excellence from the Design Expo, a forum for best practices in HCI,
ideas that are controversial, innovative, or "hard-to-publish" from alt.chi,
position papers from the Development Consortium, which looks at SIGCHI's role in serving an increasingly multidisciplinary membership, and
panels and SIGs that reflect on the foundations of our discipline, and look forward at issues affecting the community today, with topics ranging from outsourcing to the CHI Papers process.

You will also find brief summaries from Workshop organizers, Doctoral Consortium participants, Student Competition teams, and Late Breaking Results posters. If you are viewing this disc at the conference, we invite you to interact with these authors during the Highlights on Posters sessions on Wednesday and Thursday in the Commons; check your conference program for more information.On a personal note: bringing together such a large and diverse program is no small task, and I am grateful to the dedication of our venue chairs and Web master - from developing the Calls for Participation and handling the submission/review process, to dealing with my endless requests for information and scheduling conference sessions. Many, many thanks for volunteering your efforts for over a year to make this conference happen!

CHI '05 Extended Abstracts on Human Factors in Computing Systems

Designing interfaces or applications that move beyond the bounds of a single device screen enables new ways to engage with digital content. Research addressing the opportunities and challenges of interactions with multiple devices in concert is of continued focus in HCI research. To inform the future research agenda of this field, we contribute an analysis and taxonomy of a corpus of 510 papers in the cross-device computing domain. For both new and experienced researchers in the field we provide: an overview, historic trends and unified terminology of cross-device research; discussion of major and under-explored application areas; mapping of enabling technologies; synthesis of key interaction techniques spanning across multiple devices; and review of common evaluation strategies. We close with a discussion of open issues. Our taxonomy aims to create a unified terminology and common understanding for researchers in order to facilitate and stimulate future cross-device research.

/pdf/cross-device-taxonomy-survey-opportunities-and-challenges-of-2rubom7yex.pdf

Cross-Device Taxonomy: Survey, Opportunities and Challenges of Interactions Spanning Across Multiple Devices

Recommender systems are software applications that help users to find items of interest in situations of information overload. Current research often assumes a one-shot interaction paradigm, where the users' preferences are estimated based on past observed behavior and where the presentation of a ranked list of suggestions is the main, one-directional form of user interaction. Conversational recommender systems (CRS) take a different approach and support a richer set of interactions. These interactions can, for example, help to improve the preference elicitation process or allow the user to ask questions about the recommendations and to give feedback. The interest in CRS has significantly increased in the past few years. This development is mainly due to the significant progress in the area of natural language processing, the emergence of new voice-controlled home assistants, and the increased use of chatbot technology. With this paper, we provide a detailed survey of existing approaches to conversational recommendation. We categorize these approaches in various dimensions, e.g., in terms of the supported user intents or the knowledge they use in the background. Moreover, we discuss technological approaches, review how CRS are evaluated, and finally identify a number of gaps that deserve more research in the future.

A Survey on Conversational Recommender Systems

Over the last ten years there has been a growing interest around text-based chatbots, software applications interacting with humans using natural written language. However, despite the enthusiastic market predictions, ‘conversing’ with this kind of agents seems to raise issues that go beyond their current technological limitations, directly involving the human side of interaction. By adopting a Human-Computer Interaction (HCI) lens, in this article we present a systematic literature review of 83 papers that focus on how users interact with text-based chatbots. We map the relevant themes that are recurrent in the last ten years of research, describing how people experience the chatbot in terms of satisfaction, engagement, and trust, whether and why they accept and use this technology, how they are emotionally involved, what kinds of downsides can be observed in human-chatbot conversations, and how the chatbot is perceived in terms of its humanness. On the basis of these findings, we highlight open issues in current research and propose a number of research opportunities that could be tackled in future years.

The human side of human-chatbot interaction: A systematic literature review of ten years of research on text-based chatbots

The "black box'' nature of today's recommender systems raises a number of challenges for users, including a lack of trust and limited user control Providing more user control is interesting to enable end-users to help steer the recommendation process with additional input and feedback However, different users may have different preferences with regard to such control To the best of our knowledge, no research has investigated the effect of personal characteristics on visual control techniques in the music recommendation domain In this paper, we present results of a user study on the web using two different visualisation techniques (a radar chart and sliders) that allows users to control Spotify recommendations A within-subject design withLatin Square counterbalancing measures was used for the study Results indicate that the radar chart helped the participants discover a significantly higher number of new songs compared to the sliders We also found that users' experience with Spotify had an influence on their interaction with different musical attributes The participants who used Spotify frequently and users with a high individual musical sophistication interacted with the attributes significantly more with the radar chart compared to the sliders Individual musical sophistication also had a significant impact on their interaction with the interaction techniques The participants with high musical sophistication interacted significantly more with the radar chart in comparison to the sliders Based on the feedback from our participants, we provide design suggestions to further improve user control in music recommendation

Controlling Spotify Recommendations: Effects of Personal Characteristics on Music Recommender User Interfaces

Previous research has found that enabling users to control the recommendation process increases user satisfaction. However, providing additional controls also increases cognitive load, and different users have different needs for control. Therefore, in this study, we investigate the effect of two personal characteristics: musical sophistication and visual memory capacity. We designed a visual user interface, on top of a commercial music recommender, with different controls: interactions with recommendations (i.e., the output of a recommender system), the user profile (i.e., the top listened songs), and algorithm parameters (i.e., weights in an algorithm). We created eight experimental settings with combinations of these three user controls and conducted a between-subjects study (N=240), to explore the effect on cognitive load and recommendation acceptance for different personal characteristics. We found that controlling recommendations is the most favorable single control element. In addition, controlling user profile and algorithm parameters was the most beneficial setting with multiple controls. Moreover, the participants with high musical sophistication perceived recommendations to be of higher quality, which in turn lead to higher recommendation acceptance. However, we found no effect of visual working memory on either cognitive load or recommendation acceptance. This work contributes an understanding of how to design control that hits the sweet spot between the perceived quality of recommendations and acceptable cognitive load.

Effects of personal characteristics on music recommender systems with different levels of controllability

Targeted advertising reaches users based on various traits, such as demographics or behaviour. However, users are often reluctant to accept ads. We hypothesise that users are more open to targeted advertising if they can inspect, control and thereby understand the process of ad selection. We conducted a between-subjects study (N=200) to investigate to what extent four key aspects of ads (Quality, Behavioural Intention, Understanding and Attitude) may be affected by transparency and user control using a flow chart. Our results indicate that positive effects of flow charts reported from other domains may also be applicable to advertising: Using flow charts to provide transparency together with user control is found to have more positive effects on domain-specific quality measures than established, text-based approaches and using either of the techniques in isolation. The paper concludes with recommendations for practitioners aiming to improve user response to ads.

Go With the Flow: Effects of Transparency and User Control on Targeted Advertising Using Flow Charts

Critiquing-based recommender systems aim to elicit more accurate user preferences from users' feedback toward recommendations. However, systems using a graphical user interface (GUI) limit the way that users can critique the recommendation. With the rise of chatbots in many application domains, they have been regarded as an ideal platform to build critiquing-based recommender systems. Therefore, we present MusicBot, a chatbot for music recommendations, featured with two typical critiquing techniques, user-initiated critiquing (UC) and system-suggested critiquing (SC). By conducting a within-subjects (N=45) study with two typical scenarios of music listening, we compared a system of only having UC with a hybrid critiquing system that combines SC with UC. Furthermore, we analyzed the effects of four personal characteristics,musical sophistication (MS), desire for control (DFC), chatbot experience (CE), and tech savviness (TS), on the user's perception and interaction of the recommendation in MusicBot. In general, compared with UC, SC yields higher perceived diversity and efficiency in looking for songs; combining UC and SC tends to increase user engagement. Both MS and DFC positively influence several key user experience (UX) metrics of MusicBot such as interest matching, perceived controllability, and intent to provide feedback.

MusicBot: Evaluating Critiquing-Based Music Recommenders with Conversational Interaction

Music recommender systems typically offer a “one-size-fits-all” approach with the same user controls and visualizations for all users. However, the effectiveness of interactive interfaces for music recommender systems is likely to be affected by individual differences. In this paper, we first conduct a comprehensive literature review of interactive interfaces in recommender systems to motivate the need for personalized interaction with music recommender systems, and two personal characteristics, visual memory and musical sophistication. More specifically, we studied the influence of these characteristics on the design of (a) visualizations for enhancing recommendation diversity and (b) the optimal level of user controls while minimizing cognitive load. The results of three experiments show a benefit for personalizing both visualization and control elements to musical sophistication. We found that (1) musical sophistication influenced the acceptance of recommendations for user controls. (2) musical sophistication also influenced recommendation acceptance, and perceived diversity for visualizations and the UI combining user controls and visualizations. However, musical sophistication only strengthens the impact of UI on perceived diversity (moderation effect) when studying the combined effect of controls and visualizations. These results allow us to extend the model for personalization in music recommender systems by providing guidelines for interactive visualization design for music recommender systems, with regard to both visualizations and user control.

/pdf/effects-of-personal-characteristics-in-control-oriented-user-1inej5qu6u.pdf

Yucheng Jin

Papers

Controlling Spotify Recommendations: Effects of Personal Characteristics on Music Recommender User Interfaces

Effects of personal characteristics on music recommender systems with different levels of controllability

Go With the Flow: Effects of Transparency and User Control on Targeted Advertising Using Flow Charts

MusicBot: Evaluating Critiquing-Based Music Recommenders with Conversational Interaction

Effects of personal characteristics in control-oriented user interfaces for music recommender systems