Open Access
ARTICLE
Context-Aware Collaborative Filtering Framework for Rating Prediction Based on Novel Similarity Estimation
Waqar Ali1, 2, Salah Ud Din1, Abdullah Aman Khan1, Saifullah Tumrani1, Xiaochen Wang1, Jie Shao1, 3, *
1 School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, 611731, China.
2 Faculty of Information Technology, The University of Lahore, Lahore, 54000, Pakistan.
3 Sichuan Artificial Intelligence Research Institute, Yibin, 644000, China.
* Corresponding Author: Jie Shao. Email: .
Computers, Materials & Continua 2020, 63(2), 1065-1078. https://doi.org/10.32604/cmc.2020.010017
Received 04 February 2020; Accepted 23 February 2020; Issue published 01 May 2020
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.
Keywords
Cite This Article
W. Ali, S. Ud Din, A. Aman Khan, S. Tumrani, X. Wang
et al., "Context-aware collaborative filtering framework for rating prediction based on novel similarity estimation,"
Computers, Materials & Continua, vol. 63, no.2, pp. 1065–1078, 2020. https://doi.org/10.32604/cmc.2020.010017