Recommender systems (RS) commonly retrieve potential candidate items for users from a massive number of items by modeling user interests based on historical interactions. However, historical interaction data is highly sparse, and most items are long-tail items, which limits the representation learning for item discovery. This problem is further augmented by the discovery of novel or cold-start items. For example, after a user displays interest in bitcoin financial investment shows in the podcast space, a recommender system may want to suggest, e.g., a newly released blockchain episode from a more technical show. Episode correlations help the discovery, especially when interaction data of episodes is limited. Accordingly, we build upon the classical Two-Tower model and introduce the novel Multi-Source Augmentations using a Contrastive Learning framework (MSACL) to enhance episode embedding learning by incorporating positive episodes from numerous correlated semantics. Extensive experiments on a real-world podcast recommendation dataset from a large audio streaming platform demonstrate the effectiveness of the proposed framework for user podcast exploration and cold-start episode recommendation.
翻译:建议系统(RS)通常通过基于历史互动的用户兴趣模型,从大量项目中为用户获取潜在候选项目。然而,历史互动数据非常稀少,大多数项目都是长尾项目,限制了项目发现的代表性学习。发现新颖或冷启动项目使这一问题进一步加剧。例如,在用户显示有兴趣在播客空间展示比特币金融投资后,一个建议系统可能想建议,例如,从一个更技术性的节目中推出一个新释放的块链插件。Episode 相关数据有助于发现,特别是当事件的互动数据有限时。因此,我们利用传统的双向模型,并采用新型的多源演示,采用差异学习框架(MSACL),通过吸收众多相关语义的正面片段,加强插图学习。一个大型音流平台上真实世界播客的建议数据集的广泛实验,展示了拟议中的用户播客式探索和冷启动插件建议框架的有效性。