User Modeling and User-Adapted Interaction
Ontological user profiling in recommender systems
ACM Transactions on Information Systems (TOIS)
Personalised hypermedia presentation techniques for improving online customer relationships
The Knowledge Engineering Review
A hybrid approach for searching in the semantic web
Proceedings of the 13th international conference on World Wide Web
IEEE Transactions on Knowledge and Data Engineering
Web search personalization with ontological user profiles
Proceedings of the sixteenth ACM conference on Conference on information and knowledge management
Tag-based user modeling for social multi-device adaptive guides
User Modeling and User-Adapted Interaction
Using information content to evaluate semantic similarity in a taxonomy
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 1
Ontology-based user modeling for knowledge management systems
UM'03 Proceedings of the 9th international conference on User modeling
Improving the effectiveness of collaborative recommendation with ontology-based user profiles
Proceedings of the 1st International Workshop on Information Heterogeneity and Fusion in Recommender Systems
Semantic similarity in heterogeneous ontologies
Proceedings of the 22nd ACM conference on Hypertext and hypermedia
Ontologically-Enriched unified user modeling for cross-system personalization
UM'05 Proceedings of the 10th international conference on User Modeling
Gumo: the general user model ontology
UM'05 Proceedings of the 10th international conference on User Modeling
Property-based interest propagation in ontology-based user model
UMAP'12 Proceedings of the 20th international conference on User Modeling, Adaptation, and Personalization
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In this paper we address the problem of propagating user interests in ontology-based user models. Our ontology-based user model (OBUM) is devised as an overlay over the domain ontology. Using ontologies as the basis of the user profile allows the initial user behavior to be matched with existing concepts in the domain ontology. Such ontological approach to user profiling has been proven successful in addressing the cold-start problem in recommender systems, since it allows for propagation from a small number of initial concepts to other related domain concepts by exploiting the ontological structure of the domain. The main contribution of the paper is the novel algorithm for propagation of user interests which takes into account i) the ontological structure of the domain and, in particular, the level at which each domain item is found in the ontology; ii) the type of feedback provided by the user, and iii) the amount of past feedback provided for a certain domain object.