User Perceived Qualities and Acceptance of Recommender Systems:The Role of Diversity

Recommender systems have become important, as users are faced with an ever-increasing amount of information available on internet. Much of the research work on the topic has been focused on recommendation techniques, aiming at improving the accuracy of recommended items. Today, researchers use accuracy-metrics for evaluating goodness, when in fact these do not capture users' expectations and criteria for evaluating recommendation usefulness. We must ask ourselves whether a less accurate recommendation is necessarily a less valuable one for the user. To support this, we centre our investigations in this thesis on users, and explore their acceptance behaviours when using recommendations, and their perceived qualities. We present results in four areas. First, we study users' perceptions leading to the acceptance of recommendations and the possible long-term adoption of the system. We run two user studies using two online music recommenders relying on different recommendation techniques. Our results show that the perceived usefulness in terms of quality, and the perceived ease of use in terms of effort, are directly correlated with the users' acceptance of the recommendations. The results also show the necessity for low-involvement recommenders to be highly reactive, helping to take the users' search context into account. Secondly, we evaluate a behavioural recommender, where recommendations are made from implicitly expressed user preferences. We take profile sizes into account and compare such recommendations to an explicit search & browse interface. Our experiment reveals that users perceive the smaller effort required to use a behavioural recommender, but find the explicit solution to yield more diverse suggestions and gives them more control. Overall, users perceive both approaches as being satisfactory, providing the profile size is big enough. Thirdly, we analyse the impact on users' perceptions of a visual rendering. We designed an iconised representation of compound critiques, usually textual, and observed the differences in users' appreciation. Our results reveal that users prefer the visual interface, that it reduces their interaction efforts, and that users are attracted to apply the critiques more frequently in complex product domains, which have more product-features. In a fourth area, we examine the role of diversity of recommendations in users' acceptance. A first study shows that diversity is the dimension which most influences users' satisfaction. We also highlight that users have more confidence in their choice using an organised layout interface for the same perceived ease of use as with a list view, even though the organised layout creates longer interactions. For the first time in a study, we show that diversity correlates with the trust of users. In a second study, we use an eye-tracker to carry out an in-depth study of users' decision process. We show how the influence of a recommender increases throughout a user's purchase decision process until the decision is close to being taken. At this moment, we observed that users rely on the recommender to enhance their confidence in the purchase decision, and that they need diversity to prioritise the suggestions. To end our work, we propose a theoretical diversity-model for maximising users' overall satisfaction by balancing users' needs for recommendation accuracy and diversity throughout the decision process. In addition, we derive a set of design guidelines from all of the experimental results. They are elaborated around four primary axes: user effort, purchase intentions, complex systems and diversity

Beyond the ranked list: User-driven exploration and diversification of social recommendation

The beyond-relevance objectives of recommender systems have been drawing more and more attention. For example, a diversity-enhanced interface has been shown to associate positively with overall levels of user satisfaction. However, little is known about how users adopt diversity-enhanced interfaces to accomplish various real-world tasks. In this paper, we present two attempts at creating a visual diversity-enhanced interface that presents recommendations beyond a simple ranked list. Our goal was to design a recommender system interface to help users explore the different relevance prospects of recommended items in parallel and to stress their diversity. Two within-subject user studies in the context of social recommendation at academic conferences were conducted to compare our visual interfaces. Results from our user study show that the visual interfaces significantly reduced the exploration efforts required for given tasks and helped users to perceive the recommendation diversity. We show that the users examined a diverse set of recommended items while experiencing an improvement in overall user satisfaction. Also, the users' subjective evaluations show significant improvement in many user-centric metrics. Experiences are discussed that shed light on avenues for future interface designs

Exploring Social Recommendations with Visual Diversity-Promoting Interfaces

The beyond-relevance objectives of recommender systems have been drawing more and more attention. For example, a diversity-enhanced interface has been shown to associate positively with overall levels of user satisfaction. However, little is known about how users adopt diversity-enhanced interfaces to accomplish various real-world tasks. In this article, we present two attempts at creating a visual diversity-enhanced interface that presents recommendations beyond a simple ranked list. Our goal was to design a recommender system interface to help users explore the different relevance prospects of recommended items in parallel and to stress their diversity. Two within-subject user studies in the context of social recommendation at academic conferences were conducted to compare our visual interfaces. Results from our user study show that the visual interfaces significantly reduced the exploration efforts required for given tasks and helped users to perceive the recommendation diversity. We show that the users examined a diverse set of recommended items while experiencing an improvement in overall user satisfaction. Also, the users’ subjective evaluations show significant improvement in many user-centric metrics. Experiences are discussed that shed light on avenues for future interface designs

Young People and Digital Intimacies. What is the evidence and what does it mean? Where next?

The digital age makes new forms of connection possible, enabling ‘digital intimacies’ including the many practices of communicating, producing and sharing intimate content (‘sexting’; selfies; making, viewing and circulating sexual content; using hook-up apps; and searching online for advice about sex). Where young people engage in digital intimacies, policymakers have tended to respond with alarm and commissioned research premised on demonstrating negative outcomes. Young people’s take up of technologies is contrasted with previous generations and ideas of ‘healthy’, ‘natural’ and ‘normal’ sexual development which ignores and marginalises diversity of sexuality and sexual expression, and leads to campaigns that seek to supervise and regulate youth sexuality. This in turn results in legislation and censorship with consequences including blocking websites for sexual abuse support and sexual education. The government has suspended introduction of Age Verification for pornographic websites but is pressing ahead with its ‘Online Harms’ White Paper which plans for broader and more comprehensive regulatory frameworks in the interests of protecting children and young people in online spaces. The UK government has positioned itself as a world leader in developing new regulatory approaches to tackle online harms but the evidence base for those approaches is neither robust nor nuanced enough to respond to the increasing mediatisation of everyday life and sexual identity. This briefing advocates for a broader recognition of young people’s investments in digital intimacies, acknowledging what growing up and learning about sex in the digital age means for young people in order to inform future policy and practice. Policies that are informed by robust research and understandings that accommodate the nuanced practices of digital intimacy will provide the support that young people need and deserve as they navigate their media lives, develop awareness of ethical and unethical behaviour, and what is right for them

Leveraging interfaces to improve recommendation diversity

Increasing diversity in the output of a recommender system is an active research question for solving a long-tail issue. Most of the current approaches have focused on ranked list optimization to improve recommendation diversity. However, little is known about the e.ect that a visual interface can have on this issue. .is paper shows that a multidimensional visualization promotes diversity of social exploration in the context of an academic conference. Our study shows a significant difference in the exploration pa.ern between ranked list and visual interfaces. .e results show that a visual interface can help the user explore a a more diverse set of recommended items

Hybrid group recommendations for a travel service

Recommendation techniques have proven their usefulness as a tool to cope with the information overload problem in many classical domains such as movies, books, and music. Additional challenges for recommender systems emerge in the domain of tourism such as acquiring metadata and feedback, the sparsity of the rating matrix, user constraints, and the fact that traveling is often a group activity. This paper proposes a recommender system that offers personalized recommendations for travel destinations to individuals and groups. These recommendations are based on the users' rating profile, personal interests, and specific demands for their next destination. The recommendation algorithm is a hybrid approach combining a content-based, collaborative filtering, and knowledge-based solution. For groups of users, such as families or friends, individual recommendations are aggregated into group recommendations, with an additional opportunity for users to give feedback on these group recommendations. A group of test users evaluated the recommender system using a prototype web application. The results prove the usefulness of individual and group recommendations and show that users prefer the hybrid algorithm over each individual technique. This paper demonstrates the added value of various recommendation algorithms in terms of different quality aspects, compared to an unpersonalized list of the most-popular destinations

Controllability and explainability in a hybrid social recommender system

The growth in artificial intelligence (AI) technology has advanced many human-facing applications. The recommender system is one of the promising sub-domain of AI-driven application, which aims to predict items or ratings based on user preferences. These systems were empowered by large-scale data and automated inference methods that bring useful but puzzling suggestions to the users. That is, the output is usually unpredictable and opaque, which may demonstrate user perceptions of the system that can be confusing, frustrating or even dangerous in many life-changing scenarios. Adding controllability and explainability are two promising approaches to improve human interaction with AI. However, the varying capability of AI-driven applications makes the conventional design principles are less useful. It brings tremendous opportunities as well as challenges for the user interface and interaction design, which has been discussed in the human-computer interaction (HCI) community for over two decades. The goal of this dissertation is to build a framework for AI-driven applications that enables people to interact effectively with the system as well as be able to interpret the output from the system. Specifically, this dissertation presents the exploration of how to bring controllability and explainability to a hybrid social recommender system, included several attempts in designing user-controllable and explainable interfaces that allow the users to fuse multi-dimensional relevance and request explanations of the received recommendations. The works contribute to the HCI fields by providing design implications of enhancing human-AI interaction and gaining transparency of AI-driven applications

Examining the Effects of Personalized Explanations in a Multi-list Food Recommender System

In the past decade, food recipe websites have become a popular approach to find a recipe. Due to the vast amount of options, food recommender systems have been devel- oped and used to suggest appetizing recipes. However, recommending appealing meals does not necessarily imply that they are healthy. Recent studies on recommender sys- tems have demonstrated a growing interest in altering the interface, where the usage of multi-list interfaces with explanations has been explored earlier in an unsuccessful at- tempt to encourage healthier food choices. Building upon other research that highlights the ability of personalized explanations to provide a better understanding of presented recommendations, this thesis explores whether a multi-list interface with personalized explanations, which takes into account user preferences, health, and nutritional aspects, can affect users’ evaluation and perception of a food recommender system, as well as steer them towards healthier choices. A food recommender system was develop, with which single- and multi-lists, as well as non-personalized and personalized explana- tions, were compared in an online experiment (N = 163) in which participants were requested to choose recipes they liked and to answer questionnaires. The analysis re- vealed that personalized explanations in a multi-list interface were not able to increase choice satisfaction, choice difficulty, understanding or support healthier choices. Sur- prisingly, users selected healthier recipes if non-personalized rather than personalized explanations were presented alongside them. In addition, users perceived multi-lists to be more diverse and found single-list to be more satisfying.Masteroppgave i informasjonsvitenskapINFO390MASV-INF