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首页> 外文期刊>Applied Intelligence: The International Journal of Artificial Intelligence, Neural Networks, and Complex Problem-Solving Technologies >A social recommender system using item asymmetric correlation
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A social recommender system using item asymmetric correlation

机译：使用项目不对称相关性的社会推荐系统

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摘要

Recommender systems have been one of the most prominent information filtering techniques during the past decade. However, they suffer from two major problems, which degrade the accuracy of suggestions: data sparsity and cold start. The popularity of social networks shed light on a new generation of such systems, which is called social recommender system. These systems act promisingly in solving data sparsity and cold start issues. Given that social relationships are not available to every system, the implicit relationship between the items can be an adequate option to replace the constraints. In this paper, we explored the effect of combining the implicit relationships of the items and user-item matrix on the accuracy of recommendations. The new Item Asymmetric Correlation (IAC) method detects the implicit relationship between each pair of items by considering an asymmetric correlation among them. Two dataset types, the output of IAC and user-item matrix, are fused into a collaborative filtering recommender via Matrix Factorization (MF) technique. We apply the two mostly used mapping models in MF, Stochastic Gradient Descent and Alternating Least Square, to investigate their performances in the presence of sparse data. The experimental results of real datasets at four levels of sparsity demonstrate the better performance of our method comparing to the other commonly used approaches, especially in handling the sparse data.
机译：推荐系统是过去十年中最突出的信息过滤技术之一。然而，他们遭受了两个主要问题，这降低了建议的准确性：数据稀疏性和冷启动。社交网络流行揭示了新一代这样的系统，称为社会推荐系统。这些系统承诺解决数据稀疏性和冷启动问题。鉴于每个系统无法使用社交关系，项目之间的隐式关系可以是替换约束的充分选项。在本文中，我们探讨了将物品和用户项矩阵的隐式关系与建议准确性相结合的效果。新项目不对称相关（IAC）方法通过考虑它们之间的不对称相关性来检测每对项目之间的隐式关系。两个数据集类型，IAC和用户项矩阵的输出通过矩阵分解（MF）技术融合到协作过滤推荐器中。我们在MF，随机梯度下降和交替的最小二乘中应用两个主要使用的映射模型，以研究它们在稀疏数据存在下的性能。四个级别的真实数据集的实验结果表明了我们与其他常用方法相比的方法的更好性能，尤其是处理稀疏数据。

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