论文标题
探索探索动机是推荐系统的变异自动编码器
Exploration-Exploitation Motivated Variational Auto-Encoder for Recommender Systems
论文作者
论文摘要
近年来，由于公司的需求不断增长，可以帮助用户发现新的和相关的项目，从而在协作过滤技术方面进行了快速发展，以提高推荐系统的性能。但是，大多数现有文献都集中在交付与用户过去偏好中学到的用户模型相匹配的项目。一个好的推荐模型有望推荐已知喜欢的物品和新颖的尝试。在这项工作中，我们引入了一种剥削探索动机的变异自动编码器（Xplovae）进行协作过滤。为了促进个性化的建议，我们构建了特定于用户的子图，其中包含一阶接近度，可捕获观察到的用户 - 项目相互作用，以进行开发和探索的高阶接近度。分层潜在空间模型用于学习给定用户的个性化项目，以及所有用户子图的总体分布。最后，对各种现实世界数据集的实验结果清楚地证明了我们提出的模型在利用剥削和勘探建议任务方面的有效性。
Recent years have witnessed rapid developments on collaborative filtering techniques for improving the performance of recommender systems due to the growing need of companies to help users discover new and relevant items. However, the majority of existing literature focuses on delivering items which match the user model learned from users' past preferences. A good recommendation model is expected to recommend items that are known to enjoy and items that are novel to try. In this work, we introduce an exploitation-exploration motivated variational auto-encoder (XploVAE) to collaborative filtering. To facilitate personalized recommendations, we construct user-specific subgraphs, which contain the first-order proximity capturing observed user-item interactions for exploitation and the high-order proximity for exploration. A hierarchical latent space model is utilized to learn the personalized item embedding for a given user, along with the population distribution of all user subgraphs. Finally, experimental results on various real-world datasets clearly demonstrate the effectiveness of our proposed model on leveraging the exploitation and exploration recommendation tasks.
