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的新方法，该方法通过流聚类与记忆网络从不同视角生成增强向量与个性化增强向量，对用户画像及用户历史行为序列进行兴趣增强。UIE不仅显著提升了兴趣稀疏用户的模型性能，对其他用户也带来明显改善。UIE为端到端解决方案，可便捷地基于排序模型实现。此外，我们将该方案扩展至长尾物品处理，采用类似方法亦取得显著提升。我们在大规模工业推荐系统中进行了大量离线与在线实验，结果表明本模型性能显著优于其他模型，对兴趣稀疏用户尤为明显。目前UIE已在多个大规模推荐系统中全面部署并取得显著效果提升。