As people increasingly rely on interactive decision support systems to choose products and make decisions, building effective interfaces for these systems becomes more and more challenging due to the explosion of on-line information, the initial incomplete user preference and user’s cognitive and emotional limitations of information processing. How to accurately elicit user’s preference thereby becomes the main concern of current decision support systems. This paper is a survey of the typical preference elicitation methods proposed by related research works, starting from the traditional utility function elicitation and analytic hierarchy process methods, to computer aided elicitation approaches which include example critiquing, needs-oriented interaction, comparison matrix, CP-network, preferences clustering & matching and collaborative filtering.

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When searching for multi-attribute services or products, understanding and representing user's preferences is a crucial task. However, many computer tools do not afford users to adequately focus on fundamental decision objectives, reveal hidden preferences, revise conflicting preferences, or explicitly reason about tradeoffs with competing decision goals. As a result, users often fail to find the best solution. From

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.

In a previously reported user study, we found that users were able to perform decision tradeoff tasks more efficiently and commit considerably fewer errors with the example critiquing interface than with the ranked list. We concluded that example-based search tools were likely to be useful particularly for extending the scope of consumer e-commerce to more complex products where decision making is critical. This paper presents results from a follow-up user study quantifying the benefits of tradeoff support. Users were able to refine the quality of their preference structures and improve decision accuracy by up to 57 % after performing tradeoff tasks. Tradeoff support also significantly increased users’ confidence in their choices. Together, these two studies show that example critiquing enables users to more accurately find what they want and be confident in their choices, while only requiring a level of effort that is comparable to the ranked list interface. Categories and Subject Descriptors H.5.2 [Information Interfaces and Presentation]: User

We describe configuration systems using constraint problem solving formalisms where feasible products are computed by constraint problem solvers. A feasible product is a configuration of constituent components that violates none of the configuration constraints and meets users' preferences as much as possible. To help users find desirable products, a system must possess and understand users' preference model as well as value functions. A constraint-based multi-attribute optimization problem (MOP) assigns utility functions to the set of feasible configurable products so that optimal ones stand out. Due to the incompleteness and uncertainties of user's preference models, optimal solutions are difficult to compute in practical settings if systems do not constantly interact with users and refine user's preference models. While building four user-involved MOPs, we have accumulated a set of interaction principles that optimize user and system collaboration while computing optimal solutions. In particular, we will concentrate here on our approaches and solutions to address tradeoff tasks in interactive MOPS.

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.

In decision theory the process of selecting the best option is called preferential choice. Many personal, business, and professional preferential choice decisions are made every day. In these situations, a decision maker must select the optimal option among multiple alternatives. In order to do this, she must be able to analyze a model of her preferences with respect to the objectives that are important to her. Prescriptive decision theory suggests several ways to effectively develop a decision model. However, these methods often end up too tedious and complicated to apply to complex decisions that involve many objectives and alternatives. In order to help people make better decisions, an easier, more intuitive way to develop interactive models for analysis of decision contexts is needed. The application of interactive visualization techniques to this problem is an opportune solution. A visualization tool to help in preferential choice must take into account important aspects from both fields of Information Visualization and Decision Theory. There exists some proposals that claim to aid preferential

Abstract. Preference elicitation is important for any computerized system advising users about choices. Recommender systems aim to propose interesting material to users. Therefore, they must first gather user preferences. Negotiation support systems can only give meaningful bidding advice based on users ’ preferences regarding negotiable issues and interests. In general, the more detail users are willing to give the better the support will be. We investigated how four factors indluence users ’ willingness to give detail in an online preference elicitation experiment. 18 users rated 60 items (pictures/songs) with 5 levels of detail (from 3-point scale over affective feedback to free text). For each item, users could choose the desired detail level. Our results show that, of the four factors investigated (having an opinion about an item, content type of the item, familiarity with, and ownership of that item), mainly having an opinion about an item makes users give significantly more detailed feedback. Further, users with an opinion about an item use qualitatively rich affective feedback in 30 % of the cases. Our findings indicate that adaptive preference elicitation interfaces can conditionally hide and show fine grained feedback providing a simpler interface, whch can be important for smaller interfaces. Further, the fact that 30 % of the cases rated with an opinion included affective detail indicates that users are willing to give rich affective feedback when they have an opinion. 1

Negotiation support systems (NSS) help users in the complex process of reaching agreements about exchange of goods or services. A difficult issue in the development of NSS is how to extract knowledge from qualitative real-life data and embed it into the system. We present a metamodel for modeling domain, user and opponent (DUO) in NSS with focus on four main concepts: issues, preferences, interests and objective domain knowledge. We claim that (a) these concepts are essential in extracting data from unstructured sources, and (b) these concepts can be a basis for formal reasoning about user preferences and bids. We ground our meta-model in negotiation literature and data gathered with case studies and interviews. Finally, we formalize parts of the meta-model as a step towards a computationally-oriented model.