Recommender systems have been shown to help users find items of interest from among a large pool of potentially interesting items. Influence is a measure of the effect of a user on the recommendations from a recommender system. Influence is a powerful tool for understanding the workings of a recommender system. Experiments show that users have widely varying degrees of influence in ratings-base...

A recommender system is an information selection that offers preferences to users and enhances their decision-making. This commonly implemented in human-computer-interaction (HCI) intervention because of its filtering personalization. However, success rate decision-making considered low the rationale for this associated with users’ psychological reactance which causing unsuccessful intervention...

Objectives: To explore the area of groundwater that can assist to improve accessibility freshwater. Methods : We propose a machine-deep learning model based on recommender system manage and classify groundwater. Finding: The main goal our proposed approach is into multi-labels, which are drinking water (Excellent or Good) Irrigation (Poor Very Poor) with guarantee higher accuracy score. applied...

The exploitation of Recommender Systems (RS) isstill a challenge, hence it is important to explore the three correlatedattributes, such as restaurant, food, and service ratings. Therefore, thisstudy provides an in-depth review these attribute ratings using theCollaborative Filtering (CF) technique. Experiments were performed with k-NN,SVD, Slope One, Co-Clustering algorithms, while RMSE, MSE, M...