IEEE Trans Cybern. 2020 Oct;50(10):4268-4280. doi: 10.1109/TCYB.2019.2900159. Epub 2019 Mar 8.
Recommender systems are currently utilized widely in e-commerce for product recommendations and within content delivery platforms. Previous studies usually use independent features to represent item content. As a result, the relationship hidden among the content features is overlooked. In fact, the reason that an item attracts a user may be attributed to only a few set of features. In addition, these features are often semantically coupled. In this paper, we present an optimization model for extracting the relationship hidden in content features by considering user preferences. The learned feature relationship matrix is then applied to address the cold-start recommendations and content-based recommendations. It could also easily be employed for the visualization of feature relation graphs. Our proposed method was examined on three public datasets: 1) hetrec-movielens-2k-v2; 2) book-crossing; and 3) Netflix. The experimental results demonstrated the effectiveness of our method in comparison to the state-of-the-art recommendation methods.
推荐系统目前在电子商务中被广泛应用于产品推荐，也在内容分发平台中被应用。以往的研究通常使用独立的特征来表示项目内容。因此，忽略了内容特征之间隐藏的关系。事实上，一个项目吸引用户的原因可能归因于少数几套特征。此外，这些特征通常是语义上耦合的。在本文中，我们提出了一种通过考虑用户偏好来提取内容特征中隐藏关系的优化模型。然后，学习到的特征关系矩阵被应用于解决冷启动推荐和基于内容的推荐问题。它也可以很容易地用于特征关系图的可视化。我们的方法在三个公共数据集上进行了检验：1）hetrec-movielens-2k-v2；2）book-crossing；3）Netflix。实验结果表明，与最先进的推荐方法相比，我们的方法是有效的。
