P. Aruna Saraswathy,M. Thangaraj,



Ontology,NLP,Recommender System,Knowledge Graph,Incremental Learning,Hybrid model,Semantic data model,


Recommender systems are implemented as information filtering agents. In most of the conventional recommender systems, the data about domain is available in limited volumes and suggestions are made to users based on their profile information. This lead to two major problems, insufficient representation of domain knowledge, called 'data sparsity' and lack of user-item interaction, called cold start. These two issues can be addressed with ontology based recommender systems, as they cam map domain information with user preferences without losing the semantic richness of the content. This work uses knowledge based method in knowledge aware recommendations to recommend most relevant research papers in digital literature collections. It uses simple methods to construct ontology knowledge graph and uses it for training incremental k-means clustering model. Finally, learning to rank, Adarank algorithm is used to list the top most recommendations for the given user query. The experiments were conducted based on real world unstructured datasets, and results have shown that the proposed model performs well over some of the state-of-the-art baselines.


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