Recommender Systems (RSs) provide personalized recommendation service based on user interest, which are widely used in various platforms. However, there are lots of users with sparse interest due to lacking consumption behaviors, which leads to poor recommendation results for them. This problem is widespread in large-scale RSs and is particularly difficult to address. To solve this problem, we propose a novel solution named User Interest Enhancement (UIE) which enhances user interest including user profile and user history behavior sequences using the enhancement vectors and personalized enhancement vector generated based on stream clustering and memory networks from different perspectives. UIE not only remarkably improves model performance on the users with sparse interest but also significantly enhance model performance on other users. UIE is an end-to-end solution which is easy to be implemented based on ranking model. Moreover, we expand our solution and apply similar methods to long-tail items, which also achieves excellent improvement. Furthermore, we conduct extensive offline and online experiments in a large-scale industrial RS. The results demonstrate that our model outperforms other models remarkably, especially for the users with sparse interest. Until now, UIE has been fully deployed in multiple large-scale RSs and achieved remarkable improvements.

翻译：推荐系统（RSs）基于用户兴趣提供个性化推荐服务，已在各类平台广泛应用。然而，由于缺乏消费行为，大量用户存在兴趣稀疏问题，导致其推荐效果不佳。该问题在大规模推荐系统中普遍存在且难以解决。为此，我们提出一种名为用户兴趣增强（UIE）的新颖解决方案，该方案利用基于流聚类和记忆网络从不同视角生成的增强向量与个性化增强向量，对用户兴趣（包括用户画像与用户历史行为序列）进行增强。UIE不仅显著提升了兴趣稀疏用户的模型性能，同时也明显改善了其他用户的模型表现。UIE是一种端到端的解决方案，易于在排序模型基础上实现。此外，我们将该方案扩展并应用于长尾物品，同样取得了优异的效果提升。进一步地，我们在一个大规模工业推荐系统中进行了广泛的离线与在线实验。结果表明，我们的模型显著优于其他模型，尤其对于兴趣稀疏用户效果更为突出。截至目前，UIE已在多个大规模推荐系统中全面部署，并取得了显著的性能提升。