Recommendation algorithm is not only widely used in entertainment media, but also plays an important role in national strategy, such as the recommendation algorithm of byte beating company. This paper studies the personalized recommendation algorithm based on representation learning. The data in social network is complex, and the data mainly exists in various platforms. This paper introduces AI (Artificial Intelligence) algorithm to guide the algorithm of representation learning, and integrates the algorithm steps of representation learning, to realize the implementation of personalized recommendation algorithm in social network, and compares the representation learning algorithm. Finally, this paper designs a method based on heat conduction and text mining to provide users with webpage recommendations and help users better mine interesting popular webpages. Research shows that the performance of IMF is better than that of PMF because it overcomes the sparsity of data by pre-filling. The accuracy of IMF is 3.69% higher than that of PMF on the epinions data set, and 6.24% higher than that of PMF on the double data set. Rtcf, socialmf, tcars, CSIT, isrec, and hesmf have better performance than PMF and IMF. Among them, rtcf, socialmf, tcars, CSIT, isrec, and hesmf improve the MAE performance of PMF by 7.6%, 6.3%, 8.8%, 7.9%, 9.5% and 14.2%, respectively.

中文翻译：

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基于表征学习的社交网络个性化推荐算法

推荐算法不仅广泛应用于娱乐媒体，在国家战略中也发挥着重要作用，比如字节跳动公司的推荐算法。本文研究了基于表征学习的个性化推荐算法。社交网络中的数据是复杂的，数据主要存在于各种平台。本文引入AI（Artificial Intelligence）算法指导表征学习算法，并整合表征学习算法步骤，实现个性化推荐算法在社交网络中的实现，并对表征学习算法进行比较。最后，本文设计了一种基于热传导和文本挖掘的方法，为用户提供网页推荐，帮助用户更好地挖掘有趣的热门网页。研究表明，IMF 的性能优于 PMF，因为它通过预填充克服了数据的稀疏性。IMF在epinions数据集上的准确率比PMF高3.69%，在double数据集上比PMF高6.24%。Rtcf、socialmf、tcars、CSIT、isrec 和 hesmf 的性能优于 PMF 和 IMF。其中，rtcf、socialmf、tcars、CSIT、isrec、hesmf分别提高了PMF的MAE性能7.6%、6.3%、8.8%、7.9%、9.5%和14.2%。在epinions数据集上比PMF高69%，在double数据集上比PMF高6.24%。Rtcf、socialmf、tcars、CSIT、isrec 和 hesmf 的性能优于 PMF 和 IMF。其中，rtcf、socialmf、tcars、CSIT、isrec、hesmf分别提高了PMF的MAE性能7.6%、6.3%、8.8%、7.9%、9.5%和14.2%。在epinions数据集上比PMF高69%，在double数据集上比PMF高6.24%。Rtcf、socialmf、tcars、CSIT、isrec 和 hesmf 的性能优于 PMF 和 IMF。其中，rtcf、socialmf、tcars、CSIT、isrec、hesmf分别提高了PMF的MAE性能7.6%、6.3%、8.8%、7.9%、9.5%和14.2%。