Da2pl 2014 preference learning meets mcda eyke h llermeier
Preference learning is a subfield in machine learning in which the goal is to learn a predictive preference model from observed preference information. In the view of supervised learning, preference learning trains on a set of items which have preferences toward labels or other items and predicts the preferences for all items.
Contents
- Da2pl 2014 preference learning meets mcda eyke h llermeier
- Scalable collaborative bayesian preference learning mohammad emtiyaz khan
- Tasks
- Label ranking
- Instance ranking
- Object ranking
- Techniques
- Utility function
- Preference relations
- Uses
- References
While the concept of preference learning has been emerged for some time in many fields such as economics, it's a relatively new topic in Artificial Intelligence research. Several workshops have been discussing preference learning and related topics in the past decade.
Scalable collaborative bayesian preference learning mohammad emtiyaz khan
Tasks
The main task in preference learning concerns problems in "learning to rank". According to different types of preference information observed, the tasks are categorized as three main problems in the book Preference Learning:
Label ranking
In label ranking, the model has an instance space
It was observed some conventional classification problems can be generalized in the framework of label ranking problem: if a training instance
Instance ranking
Instance ranking also has the instance space
Object ranking
Object ranking is similar to instance ranking except that no labels are associated with instances. Given a set of pairwise preference information in the form
Techniques
There are two practical representations of the preference information
Utility function
If we can find a mapping from data to real numbers, ranking the data can be solved by ranking the real numbers. This mapping is called utility function. For label ranking the mapping is a function
Finding the utility function is a regression learning problem which is well developed in machine learning.
Preference relations
The binary representation of preference information is called preference relation. For each pair of alternatives (instances or labels), a binary predicate can be learned by conventional supervising learning approach. Fürnkranz, Johannes and Hüllermeier proposed this approach in label ranking problem. For object ranking, there is an early approach by Cohen et al.
Using preference relations to predict the ranking will not be so intuitive. Since preference relation is not transitive, it implies that the solution of ranking satisfying those relations would sometimes be unreachable, or there could be more than one solution. A more common approach is to find a ranking solution which is maximally consistent with the preference relations. This approach is a natural extension of pairwise classification.
Uses
Preference learning can be used in ranking search results according to feedback of user preference. Given a query and a set of documents, a learning model is used to find the ranking of documents corresponding to the relevance with this query. More discussions on research in this field can be found in Tie-Yan Liu's survey paper.
Another application of preference learning is recommender systems. Online store may analyze customer's purchase record to learn a preference model and then recommend similar products to customers. Internet content providers can make use of user's ratings to provide more user preferred contents.