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.
刘慧婷, 杨良全, 凌超, 赵鹏. 社交网络中融合社交关系和语义信息的推荐算法[J]. 模式识别与人工智能, 2018, 31(3): 236-244.
LIU Huiting, YANG Liangquan, LING Chao, ZHAO Peng. Recommendation Algorithm with Social Relations and Content Information in Social Networks. , 2018, 31(3): 236-244.
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