It has become increasingly clear that recommender systems overly focusing on short-term engagement can inadvertently hurt long-term user experience. However, it is challenging to optimize long-term user experience directly as the desired signal is sparse, noisy and manifests over a long horizon. In this work, we show the benefits of incorporating higher-level user understanding, specifically user intents that can persist across multiple interactions or recommendation sessions, for whole-page recommendation toward optimizing long-term user experience. User intent has primarily been investigated within the context of search, but remains largely under-explored for recommender systems. To bridge this gap, we develop a probabilistic intent-based whole-page diversification framework in the final stage of a recommender system. Starting with a prior belief of user intents, the proposed diversification framework sequentially selects items at each position based on these beliefs, and subsequently updates posterior beliefs about the intents. It ensures that different user intents are represented in a page towards optimizing long-term user experience. We experiment with the intent diversification framework on one of the world's largest content recommendation platforms, serving billions of users daily. Our framework incorporates the user's exploration intent, capturing their propensity to explore new interests and content. Live experiments show that the proposed framework leads to an increase in user retention and overall user enjoyment, validating its effectiveness in facilitating long-term planning. In particular, it enables users to consistently discover and engage with diverse contents that align with their underlying intents over time, thereby leading to an improved long-term user experience.

翻译：在推荐系统中，过度关注短期参与度可能会无意中损害长期用户体验，这一认识已日益明确。然而，直接优化长期用户体验具有挑战性，因为所需信号稀疏、嘈杂且存在于较长时间范围内。在本研究中，我们展示了整合更高层次的用户理解——特别是能够跨多个交互或推荐会话持续存在的用户意图——对于优化长期用户体验的整页推荐所带来的益处。用户意图主要在搜索背景下得到研究，但在推荐系统中仍很大程度上未被探索。为弥补这一差距，我们在推荐系统的最终阶段开发了一个基于概率意图的整页多样化框架。从用户意图的先验信念出发，所提出的多样化框架根据这些信念依次选择每个位置的项目，并随后更新关于意图的后验信念。该框架确保不同的用户意图在页面中得到体现，以优化长期用户体验。我们在全球最大的内容推荐平台之一（每天服务数十亿用户）上对意图多样化框架进行了实验。我们的框架融合了用户的探索意图，捕捉其探索新兴趣和内容的倾向。实时实验表明，所提出的框架能够提高用户留存率和整体用户满意度，验证了其在促进长期规划方面的有效性。特别是，它使用户能够持续发现并参与与其潜在意图相符的多样化内容，从而带来更好的长期用户体验。