Good, better, and most probable recommendations
- Machine Learning seems to offer the solution to the central problem in recommender systems: Learning to recommend interesting items from observations. However, one tends to run into similar problems each time one tries to apply out-of-the-box solutions from Machine Learning. This article relates the problem of recommendation by user modeling closely to the machine learning problem and explicates some inherent dilemmas. A few examples will illustrate specific approaches and discuss underlying assumptions on the domain or how learned hypotheses relate to requirements on the user model. The article concludes with a tentative 'checklist' that one might like to consider when thinking about to use Machine Learning in User Adaptive environments such as recommender systems.
| Document Type: | Report |
|---|---|
| Language: | English |
| Author: | Martin E. Müller |
| Parent Title (English): | Reports / Technische Berichte der Fakultät für Angewandte Informatik der Universität Augsburg |
| Issue: | 2004-17 |
| URL: | https://nbn-resolving.org/urn:nbn:de:bvb:384-opus4-304 |
| Publisher: | Universität Augsburg |
| Publication year: | 2004 |
| Keyword: | Benutzeroberfläche; adaptive user interfaces; machine learning for user modeling; maschinelles Lernen; recommender systems |
| Dewey Decimal Classification (DDC): | 0 Informatik, Informationswissenschaft, allgemeine Werke / 00 Informatik, Wissen, Systeme / 004 Datenverarbeitung; Informatik |
| Entry in this database: | 2017/01/13 |
