Ontology Retrieval models Information filtering Content-based filtering User profiles
Institute of Information Theories and Applications FOI ITHEA
Traditional content-based filtering methods usually utilize text extraction and classification techniques
for building user profiles as well as for representations of contents, i.e. item profiles. These methods have some
disadvantages e.g. mismatch between user profile terms and item profile terms, leading to low performance.
Some of the disadvantages can be overcome by incorporating a common ontology which enables representing
both the users' and the items' profiles with concepts taken from the same vocabulary.
We propose a new content-based method for filtering and ranking the relevancy of items for users, which utilizes
a hierarchical ontology. The method measures the similarity of the user's profile to the items' profiles, considering
the existing of mutual concepts in the two profiles, as well as the existence of "related" concepts, according to
their position in the ontology. The proposed filtering algorithm computes the similarity between the users' profiles
and the items' profiles, and rank-orders the relevant items according to their relevancy to each user. The method
is being implemented in ePaper, a personalized electronic newspaper project, utilizing a hierarchical ontology
designed specifically for classification of News items. It can, however, be utilized in other domains and extended
to other ontologies.