UtilSim: Iteratively Helping Users Discover Their Preferences

Conversational Recommender Systems belong to a class of knowledge based systems which simulate a customer’s interaction with a shopkeeper with the help of repeated user feedback till the user settles on a product. One of the modes for getting user feedback is Preference Based Feedback, which is especially suited for novice users(having little domain knowledge), who find it easy to express preferences across products as a whole, rather than specific product features. Such kind of novice users might not be aware of the specific characteristics of the items that they may be interested in, hence, the shopkeeper/system should show them a set of products during each interaction, which can constructively stimulate their preferences, leading them to a desirable product in subsequent interactions. We propose a novel approach to conversational recommendation, UtilSim, where utilities corresponding to products get continually updated as a user iteratively interacts with the system, helping her discover her hidden preferences in the process. We show that UtilSim, which combines domain-specific “dominance” knowledge with SimRank based similarity, significantly outperforms the existing conversational approaches using Preference Based Feedback in terms of recommendation efficiency.

[1]  Luc Lamontagne,et al.  Case-Based Reasoning Research and Development , 1997, Lecture Notes in Computer Science.

[2]  Barry Smyth,et al.  Knowledge Discovery from User Preferences in Conversational Recommendation , 2005, PKDD.

[3]  Jennifer Widom,et al.  SimRank: a measure of structural-context similarity , 2002, KDD.

[4]  Dan Ariely,et al.  Seeking Subjective Dominance in Multidimensional Space: An Explanation of the Asymmetric Dominance Effect , 1995 .

[5]  Barry Smyth,et al.  Case-Based Recommendation , 2007, The Adaptive Web.

[6]  Barry Smyth,et al.  Advances in Case-Based Reasoning , 1996, Lecture Notes in Computer Science.

[7]  Barry Smyth,et al.  A personalised TV listings service for the digital TV age , 2000, Knowl. Based Syst..

[8]  Lior Rokach,et al.  Introduction to Recommender Systems Handbook , 2011, Recommender Systems Handbook.

[9]  Tzung-Pei Hong,et al.  Next-Generation Applied Intelligence, 22nd International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2009, Tainan, Taiwan, June 24-27, 2009. Proceedings , 2009, IEA/AIE.

[10]  Alex Ferguson,et al.  An Expressive Query Language for Product Recommender Systems , 2004, Artificial Intelligence Review.

[11]  Li Chen,et al.  User-Involved Preference Elicitation for Product Search and Recommender Systems , 2008, AI Mag..

[12]  Luís Torgo,et al.  Knowledge Discovery in Databases: PKDD 2005, 9th European Conference on Principles and Practice of Knowledge Discovery in Databases, Porto, Portugal, October 3-7, 2005, Proceedings , 2005, PKDD.

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

[14]  David McSherry,et al.  Similarity and Compromise , 2003, ICCBR.

[15]  Marco Gori,et al.  ItemRank: A Random-Walk Based Scoring Algorithm for Recommender Engines , 2007, IJCAI.

[16]  Barry Smyth,et al.  Experiments in dynamic critiquing , 2005, IUI.

[17]  Bart P. Knijnenburg,et al.  Understanding the effect of adaptive preference elicitation methods on user satisfaction of a recommender system , 2009, RecSys '09.

[18]  Barry Smyth,et al.  Comparison-Based Recommendation , 2002, ECCBR.

[19]  I. Simonson,et al.  Choice Based on Reasons: The Case of Attraction and Compromise Effects , 1989 .

[20]  Alfred Kobsa,et al.  The Adaptive Web, Methods and Strategies of Web Personalization , 2007, The Adaptive Web.

[21]  Rajeev Motwani,et al.  The PageRank Citation Ranking : Bringing Order to the Web , 1999, WWW 1999.

[22]  Alexander Felfernig,et al.  Calculating Decoy Items in Utility-Based Recommendation , 2009, IEA/AIE.

[23]  Barry Smyth,et al.  A comparison of two compound critiquing systems , 2007, IUI '07.

[24]  Sergio Escalera Guerrero,et al.  Increasing Retrieval Quality in Conversational Recommenders , 2012, IEEE Transactions on Knowledge and Data Engineering.

[25]  Pearl Pu,et al.  A visual interface for critiquing-based recommender systems , 2008, EC '08.

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