Paper Title
A Novel Dynamic Personalized Recommendation Technique For Sparse Data

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