Heterogeneous information networks can naturally simulate complex objects, and they can enrich recommendation systems according to the connections between different types of objects. At present, a large number of recommendation algorithms based on heterogeneous information networks have been proposed. However, the existing algorithms cannot extract and combine the structural features in heterogeneous information networks. Therefore, this paper proposes an efficient recommendation algorithm based on heterogeneous information network, which uses the characteristics of graph convolution neural network to automatically learn node information to extract heterogeneous information and avoid errors caused by the manual search for metapaths. Furthermore, by fully considering the scoring relationship between nodes, a calculation strategy combining heterogeneous information and a scoring information fusion strategy is proposed to solve the scoring between nodes, which makes the prediction scoring more accurate. Finally, by updating the nodes, the training scale is reduced, and the calculation efficiency is improved. The study conducted a large number of experiments on three real data sets with millions of edges. The results of the experiments show that compared with PMF, SemRec, and other algorithms, the proposed algorithm improves the recommendation accuracy MAE by approximately 3% and the RMSE by approximately 8% and reduces the time consumption significantly.

中文翻译：

---

一种基于异构信息网络的高效推荐算法

异构信息网络可以自然地模拟复杂的对象，并且可以根据不同类型的对象之间的联系来丰富推荐系统。当前，已经提出了大量基于异构信息网络的推荐算法。但是，现有算法无法提取和组合异构信息网络中的结构特征。因此，本文提出了一种基于异构信息网络的高效推荐算法，该算法利用图卷积神经网络的特征，自动学习节点信息，提取异构信息，避免了人工搜索元路径带来的错误。此外，通过充分考虑节点之间的得分关系，为了解决节点之间的评分问题，提出了一种将异构信息与评分信息融合策略相结合的计算策略，从而使预测评分更加准确。最后，通过更新节点，减少了训练规模，提高了计算效率。该研究对具有数百万条边的三个真实数据集进行了大量实验。实验结果表明，与PMF，SemRec和其他算法相比，该算法将推荐精度MAE提高了约3％，将RMSE提高了约8％，并显着减少了时间消耗。减少了训练规模，提高了计算效率。该研究对具有数百万条边的三个真实数据集进行了大量实验。实验结果表明，与PMF，SemRec和其他算法相比，该算法将推荐精度MAE提高了约3％，将RMSE提高了约8％，并显着减少了时间消耗。减少了训练规模，提高了计算效率。该研究对具有数百万条边的三个真实数据集进行了大量实验。实验结果表明，与PMF，SemRec和其他算法相比，该算法将推荐精度MAE提高了约3％，将RMSE提高了约8％，并显着减少了时间消耗。