论文标题
为时尚推荐建模现场级别的因素交互
Modeling Field-level Factor Interactions for Fashion Recommendation
论文作者
论文摘要
个性化的时尚推荐旨在探索用户和时尚项目之间历史互动的模式，从而预测未来的方式。由于交互数据的稀疏性以及时尚偏好的多样性，这是具有挑战性的。为了应对挑战，本文研究了时尚域中的多个因素领域，例如颜色，样式，品牌，并试图将隐式用户项目的交互指定为现场级别。具体而言，提出了一个注意因素场相互作用图（AFFIG）方法，该方法模拟了用户因素的相互作用和跨场因子相互作用，以预测特定领域的建议概率。此外，注意机制配备了每个场的跨场因子相互作用。已经在三个电子商务时尚数据集上进行了广泛的实验，结果证明了拟议的时尚建议方法的有效性。还通过实验讨论了各种因素领域对时尚域推荐的影响。
Personalized fashion recommendation aims to explore patterns from historical interactions between users and fashion items and thereby predict the future ones. It is challenging due to the sparsity of the interaction data and the diversity of user preference in fashion. To tackle the challenge, this paper investigates multiple factor fields in fashion domain, such as colour, style, brand, and tries to specify the implicit user-item interaction into field level. Specifically, an attentional factor field interaction graph (AFFIG) approach is proposed which models both the user-factor interactions and cross-field factors interactions for predicting the recommendation probability at specific field. In addition, an attention mechanism is equipped to aggregate the cross-field factor interactions for each field. Extensive experiments have been conducted on three E-Commerce fashion datasets and the results demonstrate the effectiveness of the proposed method for fashion recommendation. The influence of various factor fields on recommendation in fashion domain is also discussed through experiments.
