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首页> 外文期刊>Physica, A. Statistical mechanics and its applications >Effects of high-order correlations on personalized recommendations for bipartite networks
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Effects of high-order correlations on personalized recommendations for bipartite networks

机译：高阶相关性对双向网络个性化推荐的影响

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摘要

In this paper, we introduce a modified collaborative filtering (MCF) algorithm, which has remarkably higher accuracy than the standard collaborative filtering. In the MCF, instead of the cosine similarity index, the user-user correlations are obtained by a diffusion process. Furthermore, by considering the second-order correlations, we design an effective algorithm that depresses the influence of mainstream preferences. Simulation results show that the algorithmic accuracy, measured by the average ranking score, is further improved by 20.45% and 33.25% in the optimal cases of MovieLens and Netflix data. More importantly. the optimal value; lambda(opt) depends approximately monotonously on the sparsity of the training set. Given a real system, we could estimate the optimal parameter according to the data sparsity, which makes this algorithm easy to be applied. In addition, two significant criteria of algorithmic performance, diversity and popularity, are also taken into account. Numerical results show that as the sparsity increases, the algorithm considering the second-order correlation can outperform the MCF simultaneously in all three criteria.
机译：在本文中，我们介绍了一种改进的协作过滤（MCF）算法，该算法比标准协作过滤具有更高的准确性。在MCF中，通过扩散过程获得用户-用户相关性，而不是余弦相似性指标。此外，通过考虑二阶相关性，我们设计了一种有效的算法，可以抑制主流偏好的影响。仿真结果表明，在MovieLens和Netflix数据的最佳情况下，以平均排名得分衡量的算法准确性进一步提高了20.45％和33.25％。更重要的是。最佳值lambda（opt）大约单调取决于训练集的稀疏性。给定一个真实的系统，我们可以根据数据稀疏性估计最佳参数，这使得该算法易于应用。另外，还考虑了算法性能的两个重要标准：多样性和流行性。数值结果表明，随着稀疏度的增加，考虑到二阶相关性的算法在所有三个条件下均能同时胜过MCF。

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