Conflict Management for Constraint-based Recommendation

Constraint-based recommendation systems are well-established in several domains like cars, computers, and financial services. Such recommendation tasks are based on sets of product constraints and customer preferences. Customer preferences reduce the number of products which are relevant for the customer. In scenarios like that it may happen that the set of customer preferences is inconsistent with the set of constraints in the recommendation system. In order to repair an inconsistency, the customer is informed about possible ways to adapt his/her preferences. There are different possibilities to present this information to the customer: a) via preferred diagnoses, b) via preferred conflicts, and c) via similar products. On the basis of the results of an empirical study we show that diagnoses, conflicts, and similar products are evaluated differently by users in terms of understandability, user satisfaction, and conflict resolution effort.

[1]  A. Tversky,et al.  The Causes of Preference Reversal , 1990 .

[2]  Alexander Felfernig,et al.  An efficient diagnosis algorithm for inconsistent constraint sets , 2011, Artificial Intelligence for Engineering Design, Analysis and Manufacturing.

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

[4]  Alexander Felfernig,et al.  Personalized Diagnosis for Over-Constrained Problems , 2013, IJCAI.

[5]  Markus Stumptner,et al.  Consistency-based diagnosis of configuration knowledge bases , 1999, Artif. Intell..

[6]  Michael J. Pazzani,et al.  Content-Based Recommendation Systems , 2007, The Adaptive Web.

[7]  Alexander Felfernig,et al.  Constraint-based recommender systems: technologies and research issues , 2008, ICEC.

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

[9]  Edward P. K. Tsang,et al.  Foundations of constraint satisfaction , 1993, Computation in cognitive science.

[10]  Alexander Felfernig,et al.  Personalized diagnoses for inconsistent user requirements , 2011, Artificial Intelligence for Engineering Design, Analysis and Manufacturing.

[11]  Gerhard Friedrich,et al.  Recommender Systems - An Introduction , 2010 .

[12]  Li Chen,et al.  Evaluating Critiquing-based Recommender Agents , 2006, AAAI.

[13]  J. B. Brooke,et al.  SUS: A 'Quick and Dirty' Usability Scale , 1996 .

[14]  Elizabeth C. Hirschman,et al.  Judgment under Uncertainty: Heuristics and Biases , 1974, Science.

[15]  D. Kahneman,et al.  Anomalies: The Endowment Effect, Loss Aversion, and Status Quo Bias , 1991 .

[16]  Gerhard Friedrich,et al.  Persuasive Recommendation: Serial Position Effects in Knowledge-Based Recommender Systems , 2007, PERSUASIVE.

[17]  Ulrich Junker,et al.  QUICKXPLAIN: Preferred Explanations and Relaxations for Over-Constrained Problems , 2004, AAAI.

[18]  Raymond Reiter,et al.  A Theory of Diagnosis from First Principles , 1986, Artif. Intell..

[19]  I. Arribas,et al.  Measuring International Economic Integration: Theory and Evidence of Globalization , 2006 .

[20]  Jon-Chao Hong,et al.  A study on thinking strategy between experts and novices of computer games , 2003, Comput. Hum. Behav..

[21]  William Samuelson,et al.  Status quo bias in decision making , 1988 .

[22]  A Diagnosis Algorithm for Inconsistent Constraint Sets , 2010 .