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首页> 外文期刊>Journal of Intelligent Information Systems >ISoTrustSeq: a social recommender system based on implicit interest, trust and sequential behaviors of users using matrix factorization
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ISoTrustSeq: a social recommender system based on implicit interest, trust and sequential behaviors of users using matrix factorization

机译：ISoTrustSeq：基于矩阵隐式化的基于用户隐性兴趣，信任和顺序行为的社交推荐系统

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摘要

Recommender systems try to propose a list of main interests of an on line social network user based on his predicted rating values. In the recent years, several methods are proposed such as Interest Social Recommendation method (ISoRec), and Social Recommendation method based on trust Sequence Matrix Factorization which employs matrix factorization techniques to address the trust propagation and sequential behaviors issues. Main drawback of these works is that they ignore implicit interest of users. Therefore, the main goal of this paper is to solve user-item rating based on the trust, sequential interest and the implicit interest of users, simultaneously. In order to solve this problem, our proposed method combines these parameters as its inputs. This method based on matrix factorization named as ISoTrustSeq. Experimental results show higher accuracy of predicted values in compared to the above-mentioned methods. Our results are also much better than these methods in terms of variation in the number of user-items features.
机译：推荐系统尝试基于在线社交网络用户的预测评分值来提出其主要兴趣的列表。近年来，提出了几种方法，例如兴趣社会推荐方法（ISoRec）和基于信任序列矩阵分解的社会推荐方法，该方法采用矩阵分解技术来解决信任传播和顺序行为问题。这些作品的主要缺点是它们忽略了用户的内在兴趣。因此，本文的主要目标是同时解决基于用户的信任，顺序兴趣和隐性兴趣的用户项目评级。为了解决这个问题，我们提出的方法将这些参数作为其输入。这种基于矩阵分解的方法称为ISoTrustSeq。实验结果表明，与上述方法相比，预测值的准确性更高。就用户项功能数量的变化而言，我们的结果也比这些方法好得多。

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