FM-Intent：通过层次多任务学习预测用户会话意图
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Authors: Sejoon Oh, Moumita Bhattacharya, Yesu Feng, Sudarshan Lamkhede, Ko-Jen Hsiao, and Justin Basilico
作者： Sejoon Oh, Moumita Bhattacharya, Yesu Feng, Sudarshan Lamkhede, Ko-Jen Hsiao, 和 Justin Basilico
Motivation
动机
Recommender systems have become essential components of digital services across e-commerce, streaming media, and social networks [1, 2]. At Netflix, these systems drive significant product and business impact by connecting members with relevant content at the right time [3, 4]. While our recommendation foundation model (FM) has made substantial progress in understanding user preferences through large-scale learning from interaction histories (please refer to this article about FM @ Netflix), there is an opportunity to further enhance its capabilities. By extending FM to incorporate the prediction of underlying user intents, we aim to enrich its understanding of user sessions beyond next-item prediction, thereby offering a more comprehensive and nuanced recommendation experience.
推荐系统已成为电子商务、流媒体和社交网络等数字服务的核心组成部分[1, 2]。在Netflix，这些系统通过在合适的时间将会员与相关内容连接起来，驱动了显著的产品和业务影响[3, 4]。虽然我们的推荐基础模型（FM）在通过从交互历史中进行大规模学习来理解用户偏好方面取得了重大进展（请参阅关于FM @ Netflix的这篇文章），但仍有机会进一步增强其能力。通过扩展FM以纳入对潜在用户意图的预测，我们旨在丰富其对用户会话的理解，超越下一个项目的预测，从而提供更全面和细致的推荐体验。
Recent research has highlighted the importance of understanding user intent in online platforms [5, 6, 7, 8]. As Xia et al. [8] demonstrated at Pinterest, predicting a user’s future intent can lead to more accurate and personalized recommendations. However, existing intent prediction approaches typically employ simple multi-task learning that adds intent prediction heads to next-item prediction models without establishing a hierarchical relationship between these tasks.
最近的研究强调了在在线平台上理解用户意图的重要性 [5, 6, 7, 8]。正如 Xia 等人 [8] 在 Pinterest 所展示的，预测用户未来的意图可以导...