Abstract
This chapter aims to provide an overview of the class of multi-criteria recommender systems, i.e., the category of recommender systems that usemulti-criteria preference ratings. Traditionally, the vast majority of recommender systems literature has focused on providing recommendations by modelling a user’s utility (or preference) for an item as a single preference rating. However, where possible, capturing richer user preferences along several dimensions—for example, capturing not only the user’s overall preference for a given movie but also her preferences for specific movie aspects (such as acting, story, or visual effects)—can provide opportunities for further improvements in recommendation quality. As a result, a number of recommendation techniques that attempt to take advantage of such multi-criteria preference information have been developed in recent years. A review of current algorithms that use multi-criteria ratings for calculating predictions and generating recommendations is provided. The chapter concludes with a discussion on open issues and future challenges for the class of multi-criteria rating recommenders.
Original language | English (US) |
---|---|
Title of host publication | Recommender Systems Handbook, Second Edition |
Publisher | Springer US |
Pages | 847-880 |
Number of pages | 34 |
ISBN (Electronic) | 9781489976376 |
ISBN (Print) | 9781489976369 |
DOIs | |
State | Published - Jan 1 2015 |
Bibliographical note
Publisher Copyright:© Springer Science+Business Media New York 2011, 2015.