Name
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.3233/AIC-120543 | AI Commun. |
Keywords | Field | DocType |
automated adaptation,sales expert,cases utility constraint,automated repair,complex product,utility constraint set,utility constraint,error-prone process,automated reproduction,constraint-based recommendation technology,constraint-based recommender system,scoring rule | Recommender system,Computer science,Nonlinear programming,Artificial intelligence,Machine learning,Knowledge acquisition | Journal |
Volume | Issue | ISSN |
26 | 1 | 0921-7126 |
Citations | PageRank | References |
3 | 0.41 | 21 |
Authors | ||
8 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Alexander Felfernig | 1 | 1121 | 110.93 |
| Stefan Schippel | 2 | 3 | 0.74 |
| Gerhard Leitner | 3 | 145 | 14.71 |
| Florian Reinfrank | 4 | 52 | 5.88 |
| Klaus Isak | 5 | 36 | 3.54 |
| Monika Mandl | 6 | 82 | 8.92 |
| Paul Blazek | 7 | 3 | 0.41 |
| Gerald Ninaus | 8 | 42 | 5.99 |