Proactive predictions to handle issues in recommendations

In today's online world users are suffering with the problem of information overload. To handle this problem, recommender systems assist users in giving required information by filtering out irrelevant information. So, most of the recommender systems mainly strive to achieve only accuracy in recommendations but this is not just what users want. Users require more coverage and diversity in recommendations mainly in the case of news domain which is highly dynamic in nature. To handle the issues of coverage and diversity we have worked on proactive predictions of those user interests which could not have been predicted by just user behavior analysis. User interest has been expanded on the basis of Concepts, sub concepts, entities, properties and relationships stored in our designed news domain ontology. Ontology design is based on news industry standards and careful study of the domain. It is also semantically annotated with context sensitive knowledge, extracted from external knowledge source DBpedia.