基于表示学习的社交网络个性化推荐算法

IF 1.5 Q2 COMPUTER SCIENCE, THEORY & METHODS
Xiaoxian Zhang, Jianpei Zhang, Jing Yang
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引用次数: 1

摘要

推荐算法不仅广泛应用于娱乐媒体,而且在国家战略中也扮演着重要的角色,例如字节跳动公司的推荐算法。本文研究了基于表示学习的个性化推荐算法。社交网络中的数据是复杂的,数据主要存在于各个平台。本文引入AI(人工智能)算法来指导表征学习的算法,整合表征学习的算法步骤,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数据集上的准确率比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和hesmf分别提高了PMF的MAE性能7.6%、6.3%、8.8%、7.9%、9.5%和14.2%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Personalized recommendation algorithm in social networks based on representation learning
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.
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来源期刊
CiteScore
2.80
自引率
23.10%
发文量
31
期刊介绍: The International Journal of Fuzzy Logic and Intelligent Systems (pISSN 1598-2645, eISSN 2093-744X) is published quarterly by the Korean Institute of Intelligent Systems. The official title of the journal is International Journal of Fuzzy Logic and Intelligent Systems and the abbreviated title is Int. J. Fuzzy Log. Intell. Syst. Some, or all, of the articles in the journal are indexed in SCOPUS, Korea Citation Index (KCI), DOI/CrossrRef, DBLP, and Google Scholar. The journal was launched in 2001 and dedicated to the dissemination of well-defined theoretical and empirical studies results that have a potential impact on the realization of intelligent systems based on fuzzy logic and intelligent systems theory. Specific topics include, but are not limited to: a) computational intelligence techniques including fuzzy logic systems, neural networks and evolutionary computation; b) intelligent control, instrumentation and robotics; c) adaptive signal and multimedia processing; d) intelligent information processing including pattern recognition and information processing; e) machine learning and smart systems including data mining and intelligent service practices; f) fuzzy theory and its applications.
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