Paper Title
A Novel Dynamic Personalized Recommendation Technique For Sparse Data
Abstract
In E-commerce, sparse data is difficult to manage. Recommendation technique is used to provide dynamic high
quality recommendation. If no value exist for given combination of dimension values, no rows exists in fact table. The
methods to make use of profiles to extend the co-relating relation, a set to reflect user's preferences or item's reputation are
relation mining of rating data, dynamic feature extraction. In Relation mining a semi co-relate relation between items rating
and profile content are utilized. Dynamic feature extraction contains set of dynamic features to describe users' multi-phase
preferences with respect to computation, accuracy and flexibility. For high quality recommendation adaptive weighting
algorithm is proposed with the help of association rule mining.
Index terms: Association rule mining, Dynamic recommendation, Dynamic feature extraction, Relation mining