@Article{cmc.2020.010017,
AUTHOR = {Waqar Ali, Salah Ud Din, Abdullah Aman Khan, Saifullah Tumrani, Xiaochen Wang, Jie Shao},
TITLE = {Context-Aware Collaborative Filtering Framework for Rating  Prediction Based on Novel Similarity Estimation},
JOURNAL = {Computers, Materials \& Continua},
VOLUME = {63},
YEAR = {2020},
NUMBER = {2},
PAGES = {1065--1078},
URL = {http://www.techscience.com/cmc/v63n2/38560},
ISSN = {1546-2226},
ABSTRACT = {Recommender systems are rapidly transforming the digital world into 
intelligent information hubs. The valuable context information associated with the users’ 
prior transactions has played a vital role in determining the user preferences for items or 
rating prediction. It has been a hot research topic in collaborative filtering-based 
recommender systems for the last two decades. This paper presents a novel Context 
Based Rating Prediction (CBRP) model with a unique similarity scoring estimation 
method. The proposed algorithm computes a context score for each candidate user to 
construct a similarity pool for the given subject user-item pair and intuitively choose the 
highly influential users to forecast the item ratings. The context scoring strategy has an 
inherent capability to incorporate multiple conditional factors to filter down the most 
relevant recommendations. Compared with traditional similarity estimation methods, 
CBRP makes it possible for the full use of neighboring collaborators’ choice on various 
conditions. We conduct experiments on three publicly available datasets to evaluate our 
proposed method with random user-item pairs and got considerable improvement in 
prediction accuracy over the standard evaluation measures. Also, we evaluate prediction 
accuracy for every user-item pair in the system and the results show that our proposed 
framework has outperformed existing methods.},
DOI = {10.32604/cmc.2020.010017}
}