Bhavithra Janakiraman^1,*, Saradha Arumugam²

Intelligent Automation & Soft Computing, Vol.26, No.2, pp. 269-280, 2020, DOI:10.31209/2019.100000150

Abstract Personalization in recommendation system has been emerging as the most predominant area in service computing. Collaborative filtering and content based approaches are two major techniques applied for recommendation. However, to improve the accuracy and enhance user satisfaction, optimization techniques such as Ant Colony and Particle Swarm Optimization were analyzed in this paper. For theoretical analysis, this paper investigates web page recommender system. For experimentation, Diabetic patient’s health records were investigated and recommendation algorithms are applied to suggest appropriate nutrition for improving their health. Experiment result shows that Particle Swarm Optimization outperforms other traditional methods with improved performance and accuracy. More >

Tao Li, Hao Li^*, Sheng Zhong, Yan Kang, Yachuan Zhang, Rongjing Bu, Yang Hu

Journal of New Media, Vol.2, No.1, pp. 21-30, 2020, DOI:10.32604/jnm.2020.09767

Abstract In view of the low interpretability of existing collaborative filtering recommendation algorithms and the difficulty of extracting information from content-based recommendation algorithms, we propose an efficient KGRS model. KGRS first obtains reasoning paths of knowledge graph and embeds the entities of paths into vectors based on knowledge representation learning TransD algorithm, then uses LSTM and soft attention mechanism to capture the semantic of each path reasoning, then uses convolution operation and pooling operation to distinguish the importance of different paths reasoning. Finally, through the full connection layer and sigmoid function to get the prediction ratings, and the items are sorted… 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 time effect of the evaluation,… More >

Syed Falahuddin Quadri¹, Xiaoyu Li^1,*, Desheng Zheng², Muhammad Umar Aftab¹, Yiming Huang³

Journal on Big Data, Vol.1, No.1, pp. 1-7, 2019, DOI:10.32604/jbd.2019.05899

Abstract Given the glut of information on the web, it is crucially important to have a system, which will parse the information appropriately and recommend users with relevant information, this class of systems is known as Recommendation Systems (RS)-it is one of the most extensively used systems on the web today. Recently, Deep Learning (DL) models are being used to generate recommendations, as it has shown state-of-the-art (SoTA) results in the field of Speech Recognition and Computer Vision in the last decade. However, the RS is a much harder problem, as the central variable in the recommendation system’s environment is the… More >

Aziguli Wulam^1,2, Yingshuai Wang^1,2, Dezheng Zhang^1,2,*, Jingyue Sang³, Alan Yang⁴

CMC-Computers, Materials & Continua, Vol.60, No.3, pp. 1003-1013, 2019, DOI:10.32604/cmc.2019.07704

Abstract In recent years, the models combining traditional machine learning with the deep learning are applied in many commodity recommendation practices. It has been proved better performance by the means of the neural network. Feature engineering has been the key to the success of many click rate estimation model. As we know, neural networks are able to extract high-order features automatically, and traditional linear models are able to extract low-order features. However, they are not necessarily efficient in learning all types of features. In traditional machine learning, gradient boosting decision tree is a typical representative of the tree model, which can… More >