Interaction design guidelines on critiquing-based recommender systems

A critiquing-based recommender system acts like an artificial salesperson. It engages users in a conversational dialog where users can provide feedback in the form of critiques to the sample items that were shown to them. The feedback, in turn, enables the system to refine its understanding of the user's preferences and prediction of what the user truly wants. The system is then able to recommend products that may better stimulate the user's interest in the next interaction cycle. In this paper, we report our extensive investigation of comparing various approaches in devising critiquing opportunities designed in these recommender systems. More specifically, we have investigated two major design elements which are necessary for a critiquing-based recommender system: critiquing coverage—one vs. multiple items that are returned during each recommendation cycle to be critiqued; and critiquing aid—system-suggested critiques (i.e., a set of critique suggestions for users to select) vs. user-initiated critiquing facility (i.e., facilitating users to create critiques on their own). Through a series of three user trials, we have measured how real-users reacted to systems with varied setups of the two elements. In particular, it was found that giving users the choice of critiquing one of multiple items (as opposed to just one) has significantly positive impacts on increasing users' decision accuracy (particularly in the first recommendation cycle) and saving their objective effort (in the later critiquing cycles). As for critiquing aids, the hybrid design with both system-suggested critiques and user-initiated critiquing support exhibits the best performance in inspiring users' decision confidence and increasing their intention to return, in comparison with the uncombined exclusive approaches. Therefore, the results from our studies shed light on the design guidelines for determining the sweetspot balancing user initiative and system support in the development of an effective and user-centric critiquing-based recommender syste

Interaction design guidelines on critiquing-based recommender systems

A critiquing-based recommender system acts like an artificial salesperson. It engages users in a conversational dialog where users can provide feedback in the form of critiques to the sample items that were shown to them. The feedback, in turn, enables the system to refine its understanding of the user's preferences and prediction of what the user truly wants. The system is then able to recommend products that may better stimulate the user's interest in the next interaction cycle. In this paper, we report our extensive investigation of comparing various approaches in devising critiquing opportunities designed in these recommender systems. More specifically, we have investigated two major design elements which are necessary for a critiquing-based recommender system: critiquing coverage-one vs. multiple items that are returned during each recommendation cycle to be critiqued; and critiquing aid-system-suggested critiques (i.e., a set of critique suggestions for users to select) vs. user-initiated critiquing facility (i.e., facilitating users to create critiques on their own). Through a series of three user trials, we have measured how real-users reacted to systems with varied setups of the two elements. In particular, it was found that giving users the choice of critiquing one of multiple items (as opposed to just one) has significantly positive impacts on increasing users' decision accuracy (particularly in the first recommendation cycle) and saving their objective effort (in the later critiquing cycles). As for critiquing aids, the hybrid design with both system-suggested critiques and user-initiated critiquing support exhibits the best performance in inspiring users' decision confidence and increasing their intention to return, in comparison with the uncombined exclusive approaches. Therefore, the results from our studies shed light on the design guidelines for determining the sweetspot balancing user initiative and system support in the development of an effective and user-centric critiquing-based recommender system

The Role of Transparency and Feedback on the Behavioral Intention to Reuse a Recommender System

The problem of choosing the right product that will best fit the consumers’ tastes and preferences also extends even in electronic commerce. However, e-commerce was able to create a technological proxy for this social filtering process that is called online Recommender Systems (RS). RS act as personalized decision guides, aiding users in decisions on matters related to personal taste. It has the potential to support and improve the quality of the decisions consumers make when searching for and selecting products and services online. However, most previous research on RS has focused on the statistical accuracy of the algorithms driving the systems, with little emphasis on interface issues and the user’s perspective. This study identified transparency and feedback as some of the possible ways to effectively evaluate recommender systems based from the users’ perspective. Thus, the goal of this research wants to focus on examining and identifying the roles of transparency and feedback in recommender systems and how it affect the user’s attitude towards the system

Recopilación y recomendación de objetos de aprendizaje

En este trabajo se presenta una línea de investigación orientada hacia el desarrollo de distintas herramientas informáticas que ayuden a la gestión de repositorios de objetos de aprendizaje. En este sentido se trabaja en dos aspectos fundamentales para mejorar su usabilidad. Un aspecto es dar soporte a las tareas de recopilación de documentos que realiza el administrador del repositorio con el objetivo de detectar documentos plausibles de ser cargados en estos repositorios juntos con sus metadatos de interés. El otro aspecto es continuar trabajando en la recomendación de objetos de aprendizaje considerando no sólo el perfil del usuario y el diseño instruccional del material educativo sino también la valoración colaborativa de grupos de estudiantes similares.Eje: Tecnología Informática Aplicada en EducaciónRed de Universidades con Carreras en Informática (RedUNCI

User effort vs. accuracy in rating-based elicitation

One of the unresolved issues when designing a recommender system is the number of ratings -- i.e., the profile length -- that should be collected from a new user before providing recommendations. A design tension exists, induced by two conflicting requirements. On the one hand, the system must collect "enough"ratings from the user in order to learn her/his preferences and improve the accuracy of recommendations. On the other hand, gathering more ratings adds a burden on the user, which may negatively affect the user experience. Our research investigates the effects of profile length from both a subjective (user-centric) point of view and an objective (accuracy-based) perspective. We carried on an offline simulation with three algorithms, and a set of online experiments involving overall 960 users and four recommender algorithms, to measure which of the two contrasting forces influenced by the number of collected ratings -- recommendations relevance and burden of the rating process -- has stronger effects on the perceived quality of the user experience. Moreover, our study identifies the potentially optimal profile length for an explicit, rating based, and human controlled elicitation strategy

Evaluating recommender systems from the user's perspective: survey of the state of the art

A recommender system is a Web technology that proactively suggests items of interest to users based on their objective behavior or explicitly stated preferences. Evaluations of recommender systems (RS) have traditionally focused on the performance of algorithms. However, many researchers have recently started investigating system effectiveness and evaluation criteria from users' perspectives. In this paper, we survey the state of the art of user experience research in RS by examining how researchers have evaluated design methods that augment RS's ability to help users find the information or product that they truly prefer, interact with ease with the system, and form trust with RS through system transparency, control and privacy preserving mechanisms finally, we examine how these system design features influence users' adoption of the technology. We summarize existing work concerning three crucial interaction activities between the user and the system: the initial preference elicitation process, the preference refinement process, and the presentation of the system's recommendation results. Additionally, we will also cover recent evaluation frameworks that measure a recommender system's overall perceptive qualities and how these qualities influence users' behavioral intentions. The key results are summarized in a set of design guidelines that can provide useful suggestions to scholars and practitioners concerning the design and development of effective recommender systems. The survey also lays groundwork for researchers to pursue future topics that have not been covered by existing method

Evaluating recommender systems from the user's perspective: survey of the state of the art

A recommender system is a Web technology that proactively suggests items of interest to users based on their objective behavior or explicitly stated preferences. Evaluations of recommender systems (RS) have traditionally focused on the performance of algorithms. However, many researchers have recently started investigating system effectiveness and evaluation criteria from users' perspectives. In this paper, we survey the state of the art of user experience research in RS by examining how researchers have evaluated design methods that augment RS's ability to help users find the information or product that they truly prefer, interact with ease with the system, and form trust with RS through system transparency, control and privacy preserving mechanisms finally, we examine how these system design features influence users' adoption of the technology. We summarize existing work concerning three crucial interaction activities between the user and the system: the initial preference elicitation process, the preference refinement process, and the presentation of the system's recommendation results. Additionally, we will also cover recent evaluation frameworks that measure a recommender system's overall perceptive qualities and how these qualities influence users' behavioral intentions. The key results are summarized in a set of design guidelines that can provide useful suggestions to scholars and practitioners concerning the design and development of effective recommender systems. The survey also lays groundwork for researchers to pursue future topics that have not been covered by existing methods