Automated repair of scoring rules in constraint-based recommender systems

Constraint-based recommender systems support customers in preference construction processes related to complex products and services. In this context, utility constraints scoring rules play an important role. They determine the order in which items products and services are presented to customers. In many cases utility constraints are faulty, i.e., calculate rankings which are not expected and accepted by marketing and sales experts. The adaptation of these constraints is extremely time-consuming and often an error-prone process. We present an approach to the automated adaptation of utility constraint sets which is based on solutions for nonlinear optimization problems. This approach increases the applicability of constraint-based recommendation technologies by allowing the automated reproduction of example item rankings specified by marketing and sales experts.

[1]  Paul Resnick,et al.  Recommender systems , 1997, CACM.

[2]  Tamás Vinkó,et al.  A comparison of complete global optimization solvers , 2005, Math. Program..

[3]  Werkstuk Bedrijfswiskunde,et al.  Recommender systems : An overview Recommender systems : An overview , 2008 .

[4]  Hsinchun Chen,et al.  A Comparison of Collaborative-Filtering Recommendation Algorithms for E-commerce , 2007, IEEE Intelligent Systems.

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

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

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

[8]  Gerhard Friedrich,et al.  An Integrated Environment for the Development of Knowledge-Based Recommender Applications , 2006, Int. J. Electron. Commer..

[9]  Jonathan L. Herlocker,et al.  Evaluating collaborative filtering recommender systems , 2004, TOIS.

[10]  Enrico Tronci.  1997 , 1997, Les 25 ans de l’OMC: Une rétrospective en photos.

[11]  A P Shimamura,et al.  Serial position effects in implicit and explicit tests of memory. , 1994, Journal of experimental psychology. Learning, memory, and cognition.

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

[13]  Gerhard Friedrich,et al.  Intelligent debugging and repair of utility constraint sets in knowledge-based recommender applications , 2008, IUI '08.

[14]  Li Chen,et al.  Survey of Preference Elicitation Methods , 2004 .

[15]  Brian W. Kernighan,et al.  AMPL: A Modeling Language for Mathematical Programming , 1993 .

[16]  Johanna D. Moore,et al.  Generating and evaluating evaluative arguments , 2006, Artif. Intell..

[17]  Christian Schmitt,et al.  Multivariate Preference Models and Decision Making with the MAUT Machine , 2003, User Modeling.

[18]  Dan Frankowski,et al.  Collaborative Filtering Recommender Systems , 2007, The Adaptive Web.

[19]  Raymond Reiter,et al.  Characterizing Diagnoses and Systems , 1992, Artif. Intell..

[20]  Aren Jansen,et al.  Content-based recommender systems for spoken documents , 2015, 2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

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

[22]  Yoram Singer,et al.  Learning to Order Things , 1997, NIPS.

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

[24]  R. A. M. O N L O P E Z D E M A N T A R A S,et al.  Retrieval, reuse, revision and retention in case-based reasoning , 2006 .

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

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

[27]  K. Lashley.  The problem of serial order in behavior , 1951 .

[28]  Robin D. Burke,et al.  Hybrid Web Recommender Systems , 2007, The Adaptive Web.

[29]  W. Edwards,et al.  Decision Analysis and Behavioral Research , 1986 .

[30]  Pat Langley,et al.  A Personalized System for Conversational Recommendations , 2011, J. Artif. Intell. Res..

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

[32]  Barry O'Sullivan,et al.  Representative Explanations for Over-Constrained Problems , 2007, AAAI.

[33]  Alexander Felfernig,et al.  Debugging user interface descriptions of knowledge-based recommender applications , 2006, IUI '06.

[34]  Alessandro Sperduti,et al.  Experimental Results on Learning Soft Constraints , 2000, KR.