Ontology-based Content Recommendation
We are developing the TV program recommender system which solves
ramp-up problems for users and items. To avoid user ramp-up problem, we
use the user clustering technique. As an user's rating histories are
accumulated, the system can find the cluster which has more similar pattern
to the user. With this cluster information and the user's rating histories,
the system generates an classifier and any items (including new
items) can be scored by the classifier. To make a novel classifier, we add
more detail features using the ontology technique. From the
ontologies which are previously built, we extract useful features in
addition to the basic EPG metadata. Our overall framework can be thought as
a hybrid approach which use collaborate filtering technique and
content-based technique with ontologies.
- The framework of our TV program recommender system
- The recommender system has basically content-based framework. In
addition to the framework, the system consists of a user clustering
part and feature extraction part with ontology.
- User clustering model
- The user cluster is built up using bottom-up approach.
- New user who has no rating history initially belongs to the
root cluster.
- According to the user's accumulated rating history, the cluster
which is belonged by the user is changed and has more similar pattern to
the user's rating pattern.
- To measure the similarity, Cosine similarity and Pearson's
correlation is used.
- Item feature supplyment from ontology
- The ontology consists of basic ontology which has TV program
information and domain specific ontologies which have detail
information in their own domains.
- From the ontologies we can extract userful specific and dynamic
features which cannot be obtained from EPG metadata.
- Classifier Algorithm
- The classifier can be implemented using linear regression,
SVM, or maximum entropy technique.