Designing Example-critiquing Interaction

In many practical scenarios, users are faced with the problem of choosing the most preferred outcome from a large set of possibilities. As people are unable to sift through them manually, decisions support systems are often used to automatically find the optimal solution. A crucial requirement for such a system is to have an accurate model of the user's preferences. Studies have shown that people are usually unable to accurately state their preferences up front, but are greatly helped by seeing examples of actual solutions. Thus, several researchers have proposed preference elicitation strategies based on example critiquing. The essential design question in example critiquing is what examples to show users in order to best help them locate their most preferred solution. In this paper, we analyze this question based on two requirements. The first is that it must stimulate the user to express further preferences by showing the range of alternatives available. The second is that the examples that are shown must contain the solution that the user would consider optimal if the currently expressed preference model was complete so that he select it as a final solution. Copyright 2004 ACM

Explicit Passive Analysis in Electronic Catalogs

We consider example-critiquing systems that help people search for their most preferred item in a large catalog. We compare 6 existing approaches in terms of user or system-centric, implicit or explicit use of preferences, assumptions used and their behavior in underconstrained and overconstrained situations. We consider several types of explicit passive analysis to guide the users in their search, that is, information offered to the user about his current search but without any action taken by the system. We suggest that such a user-centric system together with the right analysis makes the users feel more confident in their decision and reduces session time and cognitive effort. We have implemented a prototype to evaluate the impact of explicit passive analysis in (1) a query-building and (2) a preference-based approach

Explicit Trade-off and Prospective Analysis in Electronic Catalogs

We consider example-critiquing systems that help people search for their most preferred item in a large electronic catalog. We analyze how such systems can help users in the framework of four existing example-critiquing approaches (RABBIT, FindMe, Incremental Critiquing, ATA and AptDecision). In a second part we consider the use of several types of explicit passive analysis to guide the users in their search, specially in either underconstrained or overconstrained situations. We suggest that such a user-centric search system together with the right explicit passive analysis makes the users feel more confident in their decision and reduces session time and cognitive effort. Finally we present the result of a pilot study

Agile preference models based on soft constraints

An accurate model of the user's preferences is a crucial element of most decision support systems. It is often assumed that users have a well-defined and stable set of preferences that can be elicited through a set of questions. However, recent research has shown that people very often construct their preferences on the fly depending on the available decision options. Thus, their answers to a series of questions before seeing decision options are likely to be inconsistent and often lead to erroneous models. To accurately capture preference expressions as people make them, it is necessary for the preference model to be agile: it should allow decision making with an incomplete preference model, and it should let users add, retract or revise individual preferences easily. We show how constraint satisfaction and in particular soft constraints provide the right formalism to do this, and give examples of its implementation in a travel planning tool. Copyright © 2005, American Association for Artificial Intelligence (www.aaai.org). All rights reserved

Trust building with explanation interfaces

Based on our recent work on the development of a trust model for recommender agents and a qualitative survey, we explore the potential of building users' trust with explanation interfaces. We present the major results from the survey, which provided a roadmap identifying the most promising areas for investigating design issues for trust-inducing interfaces. We then describe a set of general principles derived from an in-depth examination of various design dimensions for constructing explanation interfaces, which most contribute to trust formation. We present results of a significant-scale user study, which indicate that the organization-based explanation is highly effective in building users' trust in the recommendation interface, with the benefit of increasing users' intention to return to the agent and save cognitive effor

A Visual Interface for Critiquing-based Recommender Systems

Critiquing-based recommender systems provide an efficient way for users to navigate through complex product spaces even if they are not familiar with the domain details in e-commerce environments. While recent research has mainly concentrated on methods for generating high quality compound critiques, to date there has been a lack of comprehensive investigation on the interface design issues. Traditionally the interface is textual, which shows compound critiques in plain text and may not be easily understood. In this paper we propose a new visual interface which represents various critiques by a set of meaningful icons. Results from our real-user evaluation show that the visual interface can improve the performance of critique-based recommenders by attracting users to apply the compound critiques more frequently and reducing users' interaction effort substantially when the product domain is complex. Users' subjective feedback also shows that the visual interface is highly promising in enhancing users' shopping experience

Interactive Latent Space for Mood-Based Music Recommendation

The way we listen to music has been changing fundamentally in past two decades with the increasing availability of digital recordings and portability of music players. Up to date research in music recommendation attracted millions of users to online, music streaming services, containing tens of millions of tracks (e.g. Spotify, Pandora). The main focus of up to date research in recommender systems has been algorithmic accuracy and optimization of ranking metrics. However, recent work has highlighted the importance of other aspects of the recommendation process, including explanation, transparency, control and user experience in general. Building on these aspects, this dissertation explores user interaction, control and visual explanation of music related mood metadata during recommendation process. It introduces a hybrid recommender system that suggests music artists by combining mood-based and audio content filtering in a novel interactive interface. The main vehicle for exploration and discovery in music collection is a novel visualization that maps moods and artists in the same, latent space, built upon reduced dimensions of high-dimensional artist-mood associations. It is not known what the reduced dimensions represent and this work uses hierarchical mood model to explain the constructed space. Results of two user studies, with over 200 participants each, show that visualization and interaction in a latent space improves acceptance and understanding of both metadata and item recommendations. However, too much of either can result in cognitive overload and a negative impact on user experience. The proposed visual mood space and interactive features, along with the aforementioned findings, aim to inform design of future interactive recommendation systems

Design and evaluation issues for user-centric online product search

Nowadays more and more people are looking for products online, and a massive amount of products are being sold through e-commerce systems. It is crucial to develop effective online product search tools to assist users to find their desired products and to make sound purchase decisions. Currently, most existing online product search tools are not very effective in helping users because they ignore the fact that users only have limited knowledge and computational capacity to process the product information. For example, a search tool may ask users to fill in a form with too many detailed questions, and the search results may either be too minimal or too vast to consider. Such system-centric designs of online product search tools may cause some serious problems to end-users. Most of the time users are unable to state all their preferences at one time, so the search results may not be very accurate. In addition, users can either be impatient to view too much product information, or feel lost when no product appears in the search results during the interaction process. User-centric online product search tools can be developed to solve these problems and to help users make buying decisions effectively. The search tool should have the ability to recommend suitable products to meet the user's various preferences. In addition, it should help the user navigate the product space and reach the final target product without too much effort. Furthermore, according to behavior decision theory, users are likely to construct their preferences during the decision process, so the tool should be designed in an interactive way to elicit users' preferences gradually. Moreover, it should be decision supportive for users to make accurate purchasing decisions even if they don't have detail domain knowledge of the specific products. To develop effective user-centric online product search tools, one important task is to evaluate their performance so that system designers can obtain prompt feedback. Another crucial task is to design new algorithms and new user interfaces of the tools so that they can help users find the desired products more efficiently. In this thesis, we first consider the evaluation issue by developing a simulation environment to analyze the performance of generic product search tools. Compared to earlier evaluation methods that are mainly based on real-user studies, this simulation environment is faster and less expensive. Then we implement the CritiqueShop system, an online product search tool based on the well-known critiquing technique with two aspects of novelties: a user-centric compound critiquing generation algorithm which generates search results efficiently, and a visual user interface for enhancing user's satisfaction degree. Both the algorithm and the user interface are validated by large-scale comparative real-user studies. Moreover, the collaborative filtering approach is widely used to help people find low-risk products in domains such as movies or books. Here we further propose a recursive collaborative filtering approach that is able to generate search results more accurately without requiring additional effort from the users

Improving user confidence in decision support systems for electronic catalogs

Decision support systems for electronic catalogs assist users in making the right decision from a set of possible choices. Common examples of decision making include shopping, deciding where to go for holidays, or deciding your vote in an election. Current research in the field is mainly focused on improving such systems in terms of decision accuracy, i.e. the ratio of correct decisions out of the total number of decisions taken. However, it has been widely recognized recently that another important dimension to consider is how to improve decision confidence, i.e. the certainty of the decision maker that she has made the best decision. We first review multi-attribute decision theory –the underlying framework for electronic catalogs– and present the state-of-the-art research in e-catalogs. We then describe objective and subjective measures to evaluate such systems, and propose a system baseline for achieving more accurate and meaningful comparative evaluations. We propose a framework to study the building of decision confidence within the query-feedback search interaction model, and use it to compare different types of system feedback proposed in the literature. We argue that different types of system feedback based on constraints (e.g. conflict and corrective feedback), even if not novel as such, can be combined in order to improve decision confidence. This claim is further validated by simulations and experimental evaluation comparing constraint-based feedback to ranked list feedback