Present study on recommender systems mainly focuses on the logical nature of the existence or non-existence of a priority relationship between the user and data item, regardless of the ratio or implicative relationship based on statistics between users and data items in a particular context. Therefore, this report proposes a new approach to recommender systems based on data variation trends; such method will help form a new approach to recommender systems on basis of knowledge available in the form of implicity by computation of partial derivatives for interestingness measurements. In addition, experiments aim at evaluating the effectiveness of the proposed model with traditional models based on using MSWeb dataset as empirical data, comparing and discussing the results obtained from the proposed model.