Implementation of Category Based Recommendation Module of SEP Architecture Using PBTA
Recommender systems are the software agents which are widely used to handle the problem of information overload. Most recommender systems employ collaborative-filtering or content based methods to suggest new items of interest for users. Both these methods have complementary advantages and disadvantage but independently they fail to provide good recommendations in many circumstances. In this paper the authors discuss standard architectural framework Semantic Enhanced Personalizer (SEP) which combines three recommendation techniques i.e., original, semantic and category based. This framework overcomes problem of existing recommender systems such as cold-start and sparsity.