论文标题
基于复杂网络的相似性模型的推荐方法
A Recommendation Approach based on Similarity-Popularity Models of Complex Networks
论文作者
论文摘要
推荐系统已成为在线服务和商品的提供商和用户的重要工具，尤其是随着互联网使用访问信息和购买产品和服务的增加。这项工作提出了一种基于相似性模型产生的复杂网络来预测的新型建议方法。我们首先构建一个网络模型，该模型将用户和项目作为观察到的评分中的节点作为节点，然后使用它来预测看不见的评分。探索了使用具有隐藏度量空间和点产品相似性的相似性占用性模型产生准确评级预测的前景。实施了所提出的方法，并在实验中与来自各个域的21个数据集上的基线和最先进的建议方法进行了比较。实验结果表明，所提出的方法产生了准确的预测，并且胜过现有方法。我们还表明，所提出的方法在低维度中产生了卓越的结果，证明了其在数据可视化和探索方面的有效性。
Recommender systems have become an essential tool for providers and users of online services and goods, especially with the increased use of the Internet to access information and purchase products and services. This work proposes a novel recommendation method based on complex networks generated by a similarity-popularity model to predict ones. We first construct a model of a network having users and items as nodes from observed ratings and then use it to predict unseen ratings. The prospect of producing accurate rating predictions using a similarity-popularity model with hidden metric spaces and dot-product similarity is explored. The proposed approach is implemented and experimentally compared against baseline and state-of-the-art recommendation methods on 21 datasets from various domains. The experimental results demonstrate that the proposed method produces accurate predictions and outperforms existing methods. We also show that the proposed approach produces superior results in low dimensions, proving its effectiveness for data visualization and exploration.
