Abstract Collaborative filtering is a widely adopted approach in recommendation. However, sparse data remain the main obstacle to provide high quality recommendations. To address this issue, a method is proposed to improve the performance of collaborative filtering recommendations by integrating sparse action records data generated by users, the social information among items and the content information of these items. Matrix factorization technique is adopted to map the user action matrix and item social relations into the low-dimensional latent feature space to provide an explicit interpretation of factorization on item social relations and analyze the influence of social relations of item on user action preferences. Meanwhile, to learn more effective features from the item content, a social factor regularized stacked denoising autoencoder model is utilized and it is an extension of conventional deep learning model. Experimental results on the Tencent blog and Twitter datasets show that the proposed model outperforms several traditional methods in terms of recall and average precision, and it improves the recommendation efficiency effectively.
Fund:Supported by National Natural Science Foundation of China(No.61202227,61602004)
Corresponding Authors:
YANG Liangquan, master student. His research interests include machine learning and data mining.
About author:: LIU Huiting, Ph.D., associate professor. Her research interests include data mining and machine learning.LING Chao, master student. His research interests include machine learning and data mining.ZHAO Peng, Ph.D., associate professor. Her research interests include intelligent information processing and machine learning.
LIU Huiting,YANG Liangquan,LING Chao等. Recommendation Algorithm with Social Relations and Content Information in Social Networks[J]. , 2018, 31(3): 236-244.
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2018, Vol. 31 
