基于因子分解机的信任感知商品推荐

发布时间：2018-06-14 00:20

  本文选题：电子商务 + 商品推荐　； 参考：《山东大学学报(理学版)》2016年01期

【摘要】：数据稀疏和运行速度慢是个性化推荐系统面临的难题。为了有效利用用户历史行为,基于用户的评分记录识别出用户感兴趣的内容,并结合用户间的信任关系,提出使用因子分解机(factorization machine,FM)模型进行评分预测。FM具有线性时间复杂度,并且对于稀疏的数据具有很好的学习能力,因而能进行快速推荐。试验结果表明,与传统方法相比,基于因子分解机的商品推荐方法的准确度有明显提高。
[Abstract]:Sparse data and slow running speed are the problems faced by personalized recommendation system. In order to utilize user's historical behavior effectively, the content of user's interest is identified based on the user's score record, and combining with the trust relationship between users, it is proposed that the factorization machine factorization (factorization machine FM) model is used to predict the score of .FM with linear time complexity. And the sparse data has the very good learning ability, therefore can carry on the fast recommendation. The experimental results show that the accuracy of the commodity recommendation method based on factor decomposition machine is obviously improved compared with the traditional method.
【作者单位】： 河池学院计算机与信息工程学院;武汉大学计算机学院;
【基金】：广西高校科学技术研究项目(KY2015LX338)
【分类号】：TP391.3
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本文编号：2016165