Name
Abstract | ||
|---|---|---|
In contrast to customers of bricks and mortar stores, users of online selling environments are not supported by human sales
experts. In such situations recommender applications help to identify the products and/or services that fit the user’s wishes
and needs. In order to successfully apply recommendation technologies we have to develop an in-depth understanding of decision
strategies of users. These decision strategies are explained in different models of human decision making. In this paper we
provide an overview of selected models and discuss their importance for recommender system development. Furthermore, we provide
an outlook on future research issues.
|
Year | DOI | Venue |
|---|---|---|
2011 | 10.1007/s10844-010-0134-3 | Active Media Technology |
Keywords | Field | DocType |
online selling environment,decision strategy,mortar store,future research issue,different model,consumer decision,recommender system development,situations recommender application,knowledge-based recommendation,in-depth understanding,human sales expert,knowledge-based recommendation · interactive selling · consumer buying behavior · consumer decision making,human decision,recommender system,knowledge base | Recommender system,Decision analysis,Computer science,Decision support system,Knowledge management,Business decision mapping,R-CAST,Decision engineering | Journal |
Volume | Issue | ISSN |
37 | 1 | 0925-9902 |
Citations | PageRank | References |
12 | 0.75 | 16 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Monika Mandl | 1 | 82 | 8.92 |
| Alexander Felfernig | 2 | 1121 | 110.93 |
| Erich Teppan | 3 | 84 | 6.73 |
| Monika Schubert | 4 | 88 | 8.64 |