Name
Abstract | ||
|---|---|---|
Preference-based item recommendation is an important technique employed by online product cata- logs for recommending items to buyers. Whereas the basic mathematical mechanisms used for computing value functions from stated preferences are relatively simple, developers of online catalogs need e xible formalisms that support the description of a wide range of value functions and map to scalable imple- mentations for performing the required ltering and evaluation operations. This paper introduces an XML language for describing simple value functions that allow emulating the behavior of commercial preference-based item recommendation applications. We also discuss how the required scoring opera- tions can be implemented on top of a commercial RDBMS, and present directions for future research. |
Year | Venue | Keywords |
|---|---|---|
2001 | IEEE Data Eng. Bull. | value function |
Field | DocType | Volume |
Data mining,Information retrieval,Computer science,Preference learning,Scalability | Journal | 24 |
Issue | Citations | PageRank |
3 | 7 | 0.59 |
References | Authors | |
10 | 2 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Markus Stolze | 1 | 184 | 34.39 |
| Walid Rjaibi | 2 | 121 | 10.80 |