Survey of Preference Elicitation Methods

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.

[1]  Robin Burke,et al.  Knowledge-based recommender systems , 2000 .

[2]  Hideo Shimazu,et al.  ExpertClerk: Navigating Shoppers Buying Process with the Combination of Asking and Proposing , 2001, IJCAI.

[3]  Pratyush Kumar,et al.  Evaluating example-based search tools , 2004, EC '04.

[4]  John Riedl,et al.  GroupLens: an open architecture for collaborative filtering of netnews , 1994, CSCW '94.

[5]  M. Bohanec,et al.  The Analytic Hierarchy Process , 2004 .

[6]  Thomas L. Saaty.  Fundamentals of decision making and priority theory , 2000 .

[7]  Eric J. Johnson,et al.  The adaptive decision maker , 1993 .

[8]  L. Beach.  Broadening the Definition of Decision Making: The Role of Prechoice Screening of Options , 1993 .

[9]  Peter Haddawy,et al.  Toward Case-Based Preference Elicitation: Similarity Measures on Preference Structures , 1998, UAI.

[10]  Mark Rosenstein,et al.  Recommending and evaluating choices in a virtual community of use , 1995, CHI '95.

[11]  T. L. Saaty.  A Scaling Method for Priorities in Hierarchical Structures , 1977 .

[12]  Valerie J. Trifts,et al.  Consumer Decision Making in Online Shopping Environments: The Effects of Interactive Decision Aids , 2000 .

[13]  Henry Lieberman,et al.  Intelligent profiling by example , 2001, IUI '01.

[14]  Ronen I. Brafman,et al.  UCP-Networks: A Directed Graphical Representation of Conditional Utilities , 2001, UAI.

[15]  Kristian J. Hammond,et al.  Knowledge-Based Navigation of Complex Information Spaces , 1996, AAAI/IAAI, Vol. 1.

[16]  T. Saaty,et al.  The Analytic Hierarchy Process , 1985 .

[17]  Peter Haddawy,et al.  The Decision-Theoretic Video Advisor* , 1998 .

[18]  Greg Linden,et al.  Interactive Assessment of User Preference Models: The Automated Travel Assistant , 1997 .

[19]  Kristian J. Hammond,et al.  The FindMe Approach to Assisted Browsing , 1997, IEEE Expert.

[20]  Yuval Shahar,et al.  Utility Elicitation as a Classification Problem , 1998, UAI.

[21]  Ronen I. Brafman,et al.  CP-nets: A Tool for Representing and Reasoning withConditional Ceteris Paribus Preference Statements , 2011, J. Artif. Intell. Res..

[22]  R. L. Keeney,et al.  Decisions with Multiple Objectives: Preferences and Value Trade-Offs , 1977, IEEE Transactions on Systems, Man, and Cybernetics.

[23]  John Riedl,et al.  E-Commerce Recommendation Applications , 2004, Data Mining and Knowledge Discovery.

[24]  Giuseppe Carenini,et al.  Constructed Preferences and Value-focused Thinking: Implications for AI research on Preference Elicitation , 2002 .

[25]  Sean M. McNee,et al.  Getting to know you: learning new user preferences in recommender systems , 2002, IUI '02.

[26]  Markus Stolze,et al.  Dealing with Learning in eCommerce Product Navigation and Decision Support : The Teaching Salesman Problem , 2003 .

[27]  J. Payne,et al.  Measuring Constructed Preferences: Towards a Building Code , 1999 .

[28]  Ralph L. Keeney,et al.  Decisions with multiple objectives: preferences and value tradeoffs , 1976 .

[29]  P. Slovic.  The Construction of Preference , 1995 .

[30]  Craig Boutilier,et al.  A Constraint-Based Approach to Preference Elicitation and Decision Making , 1997 .

[31]  Pattie Maes,et al.  Social information filtering: algorithms for automating “word of mouth” , 1995, CHI '95.

[32]  Markus Stolze,et al.  Well-integrated needs-oriented recommender components regarded as helpful , 2004, CHI EA '04.