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

Diversity-Enhanced Recommendation Interface and Evaluation

The beyond accuracy user experience of using recommender system is drawing more and more attention. For example, the system interface has been shown to associate positively with overall levels of user satisfaction. However, little is known about how the interfaces can constitute the user experience and the social interactions. In this paper, I plan to propose a visual diversity-enhanced interface that supports the user to inspect and control the multi-relevance recommendations. The goal is to let the users explore the different relevance prospects of recommended items in parallel and to stress their diversity. Two preliminary user studies with real-life tasks were conducted to compare the visual interface to a standard ranked list interface. The users» subjective evaluations show significant improvement in many metrics. I further show that the users explored a diverse set of recommended items while experiencing an increase in overall user satisfaction. A user-centered evaluation was used to reveal the mediating effects between the subjective and objective conceptual components. The future plans are discussed to extend the current findings

Reseach on Cloud Computing Service Oriented Architecture (雲端運算服務導向架構之研究 楊建民 1 蔡鈞華 2 劉俊宏 3)

With the development of cloud computing infrastructure, how to combine enterprise information services with external cloud computing resources is an important issue important research topic. At present, some major service providers in the market, such as Google, IBM and HP, etc. It strives to promote and provide powerful cloud computing capabilities and a complete application service environment; however, the current cloud computing The overall application and data are placed on an external platform, causing doubts about the transfer of control rights and information security when enterprises introduce cloud computing. consider.. This research adopts Google Cloud Computing Environment, combines Cloud Computing and Service Oriented Architecture to develop a four-dimensional Layered service platform, in which the application interface layer takes advantage of the open platform to reduce the burden of service interface development. center The control layer connects the service provider and the service demander, controls the complete service process, and uses external cloud computing and Resources in the Data Center. The service supply layer combines various application services and data sources, by separating computing and data This method allows service demanders to maintain control over data, so as to solve the doubts about information security when enterprises introduce cloud architecture. consider. The infrastructure layer can be connected to cloud computing resources, and the service architecture and computing proposed by various cloud operators can be used in the future Capability and storage resources, and according to the advantages and characteristics of individual platforms, give full play to the benefits of cloud computing architecture. Finally, this study demonstrates the feasibility and integration of this service platform by developing the application of digital learning community websites The advantages of external cloud computing services, and future research directions

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

A personalized people recommender system using global search approach

The goal of people recommender system is to generate meaningful social suggestion to users. The abundant data are the key factor in fulfilling a recommendation task, but the cost of user data in a real-world system is high. In this paper, we propose a novel approach that integrates a global search result with a personalized people recommendation system. Our approach utilizes the user identity as a query keyword and processes the search results through five different customized parsers. This approach solves the cold-start issue in recommendation systems and leverages the cross-domain information in order to provide a better recommendation result. To test our approach, we embedded it into an existing conference navigator system then deployed the system at two international conferences. The survey results indicate largely positive feedback about the system's effectiveness. Our study results also shed some light on the social interactions that take place at an academic conference

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

The effects of controllability and explainability in a social recommender system

In recent years, researchers in the field of recommender systems have explored a range of advanced interfaces to improve user interactions with recommender systems. Some of the major research ideas explored in this new area include the explainability and controllability of recommendations. Controllability enables end users to participate in the recommendation process by providing various kinds of input. Explainability focuses on making the recommendation process and the reasons behind specific recommendation more clear to the users. While each of these approaches contributes to making traditional “black-box” recommendation more attractive and acceptable to end users, little is known about how these approaches work together. In this paper, we investigate the effects of adding user control and visual explanations in a specific context of an interactive hybrid social recommender system. We present Relevance Tuner+, a hybrid recommender system that allows the users to control the fusion of multiple recommender sources while also offering explanations of both the fusion process and each of the source recommendations. We also report the results of a controlled study (N = 50) that explores the impact of controllability and explainability in this context

Designing Explanation Interfaces for Transparency and Beyond

In this work-in-progress paper, we presented a participatory process of designing explanation interfaces for a social recommender system with multiple explanatory goals. We went through four stages to identify the key components of the recommendation model, expert mental model, user mental model, and target mental model. We reported the results of an online survey of current system users (N=14) and a controlled user study with a group of target users (N=15). Based on the findings, we proposed five set of explanation interfaces for five recommendation models (N=25) and discussed the user preference of the interface prototypes

Evaluating Visual Explanations for Similarity-Based Recommendations: User Perception and Performance

Recommender system helps users to reduce information overload. In recent years, enhancing explainability in recommender systems has drawn more and more attention in the field of Human-Computer Interaction (HCI). However, it is not clear whether a user-preferred explanation interface can maintain the same level of performance while the users are exploring or comparing the recommendations. In this paper, we introduced a participatory process of designing explanation interfaces with multiple explanatory goals for three similarity-based recommendation models. We investigate the relations of user perception and performance with two user studies. In the first study (N=15), we conducted card-sorting and semi-interview to identify the user preferred interfaces. In the second study (N=18), we carry out a performance-focused evaluation of six explanation interfaces. The result suggests that the user-preferred interface may not guarantee the same level of performance

Explaining Social Recommendations to Casual Users: Design Principles and Opportunities

Recommender systems have become popular in recent years, and ordinary users are more likely to rely on such service when completing various daily tasks. The need to design and build explainable recommender interfaces is increasing rapidly. Most of the designs of such explanations are intended to reflect the underlying algorithms by which the recommendations are computed. These approaches have been shown to be useful for obtaining system transparency and trust. However, little is known about how to design explanation interfaces for causal (non-expert) users to achieve different explanatory goals. As a first step toward understanding the user interface design factors, we conducted an international (across 13 countries) online survey of 14 active users of a social recommender system. This study captures user feedback in the field and frames it in terms of design principles and opportunities