Waqar Ali^{1, 2}, Salah Ud Din¹, Abdullah Aman Khan¹, Saifullah Tumrani¹, Xiaochen Wang¹, Jie Shao^{1, 3, *}

CMC-Computers, Materials & Continua, Vol.63, No.2, pp. 1065-1078, 2020, DOI:10.32604/cmc.2020.010017 - 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… More >

C. Raja Kumar¹, VE. Jayanthi²

Computer Systems Science and Engineering, Vol.34, No.3, pp. 123-129, 2019, DOI:10.32604/csse.2019.34.123

Abstract Collaborative Filtering (CF) is one of the popular methodology in recommender systems. It suffers from the data sparsity problem, recommendation inaccuracy and big-error in predictions. In this paper, the efficient advisory tool is implemented for the younger generation to choose their right career based on their knowledge. It acquires the notions of indiscernible relation from Fuzzy Rough Sets Theory (FRST) and propose a novel algorithm named as Fuzzy Rough Set Theory Based Collaborative Filtering Algorithm (FRSTBCF). To evaluate the model, data is prepared using the cross validation method. Based on that, ratings are evaluated by… More >

Min Cao¹, Sijing Zhou¹, Honghao Gao^1,2,3

Intelligent Automation & Soft Computing, Vol.25, No.3, pp. 595-604, 2019, DOI:10.31209/2019.100000114

Abstract Recommender methods using reviews have become an area of active research in e-commerce systems. The use of auxiliary information in reviews as a way to effectively accommodate sparse data has been adopted in many fields, such as the product field. The existing recommendation methods using reviews typically employ aspect preference; however, the characteristics of product reviews are not considered adequate. To this end, this paper proposes a novel recommendation approach based on using product attributes to improve the efficiency of recommendation, and a hybrid collaborative filtering is presented. The product attribute model and a new More >

Yanmei Zhang¹, Xiao Geng², Shuiguang Deng³

Intelligent Automation & Soft Computing, Vol.25, No.3, pp. 513-525, 2019, DOI:10.31209/2019.100000108

Abstract With the development of service computing technologies, the online services are massive and disordered now. How to find appropriate services quickly and build a more powerful composed service according to user interests has been a research focus in recent years. Current service recommendation algorithms often directly follow the traditional recommendation framework of ecommerce, which cannot effectively assist users to complete dynamic online business construction. Therefore, a novel service recommendation approach named UISCS (User-Interest- initial Services-Correlation-successor Services) is proposed, which is designed for interactive scenario of service composition, and it mines the user implicit interests and More >

Weijin Jiang^1,2,3, Jiahui Chen^1,*, Yirong Jiang^4,*, Yuhui Xu¹, Yang Wang¹, Lina Tan¹, Guo Liang⁵

CMC-Computers, Materials & Continua, Vol.61, No.2, pp. 849-859, 2019, DOI:10.32604/cmc.2019.05932

Abstract Aiming at the problem that the traditional collaborative filtering recommendation algorithm does not fully consider the influence of correlation between projects on recommendation accuracy, this paper introduces project attribute fuzzy matrix, measures the project relevance through fuzzy clustering method, and classifies all project attributes. Then, the weight of the project relevance is introduced in the user similarity calculation, so that the nearest neighbor search is more accurate. In the prediction scoring section, considering the change of user interest with time, it is proposed to use the time weighting function to improve the influence of the More >

Yijun Tian ^{1, *}, Waii Ng¹, Jialiang Cao¹, Suzanne McIntosh¹

CMC-Computers, Materials & Continua, Vol.61, No.2, pp. 465-479, 2019, DOI:10.32604/cmc.2019.07818

Abstract In the field of Computer Science, software developers need to use a wide array of social collaborative platforms for learning and cooperating. The most popular ones are GitHub and Stack Overflow. Existing platforms only support search queries to extract relevant repository information from GitHub, or questions and answers from Stack Overflow. This ignores the valuable coder-related part-who are the top experts (geek talents) in a specific area? This information is important to companies, open source projects, and to those who want to learn from an expert role model. Thus, how to find the right developers More >

Sheng Bin^1,*, Gengxin Sun¹, Ning Cao², Jinming Qiu², Zhiyong Zheng³, Guohua Yang⁴, Hongyan Zhao⁵, Meng Jiang⁶, Lina Xu⁷

CMC-Computers, Materials & Continua, Vol.60, No.2, pp. 659-674, 2019, DOI:10.32604/cmc.2019.05858

Abstract Recommendation system is one of the most common applications in the field of big data. The traditional collaborative filtering recommendation algorithm is directly based on user-item rating matrix. However, when there are huge amounts of user and commodities data, the efficiency of the algorithm will be significantly reduced. Aiming at the problem, a collaborative filtering recommendation algorithm based on multi-relational social networks is proposed. The algorithm divides the multi-relational social networks based on the multi-subnet complex network model into communities by using information dissemination method, which divides the users with similar degree into a community. More >

Gongshen Liu¹, Kui Meng^1,*, Jiachen Ding¹, Jan P. Nees¹, Hongyi Guo¹, Xuewen Zhang¹

CMC-Computers, Materials & Continua, Vol.58, No.1, pp. 101-120, 2019, DOI:10.32604/cmc.2019.03898

Abstract Collaborative filtering is the most popular approach when building recommender systems, but the large scale and sparse data of the user-item matrix seriously affect the recommendation results. Recent research shows the user’s social relations information can improve the quality of recommendation. However, most of the current social recommendation algorithms only consider the user's direct social relations, while ignoring potential users’ interest preference and group clustering information. Moreover, project attribute is also important in item rating. We propose a recommendation algorithm which using matrix factorization technology to fuse user information and project information together. We first… More >

Mengwei Hou¹, Rong Wei^1,*, Tiangang Wang¹, Yu Cheng², Buyue Qian³

CMC-Computers, Materials & Continua, Vol.56, No.1, pp. 137-149, 2018, DOI:10.3970/cmc.2018.02438

Abstract Collaborative filtering (CF) methods are widely adopted by existing medical recommendation systems, which can help clinicians perform their work by seeking and recommending appropriate medical advice. However, privacy issue arises in this process as sensitive patient private data are collected by the recommendation server. Recently proposed privacy-preserving collaborative filtering methods, using computation-intensive cryptography techniques or data perturbation techniques are not appropriate in medical online service. The aim of this study is to address the privacy issues in the context of neighborhood-based CF methods by proposing a Privacy Preserving Medical Recommendation (PPMR) algorithm, which can protect… More >