Sequential recommendation is one of the most important tasks in recommender systems, which aims to recommend the next interacted item with historical behaviors as input. Traditional sequential recommendation always mainly considers the collected positive feedback such as click, purchase, etc. However, in short-video platforms such as TikTok, video viewing behavior may not always represent positive feedback. Specifically, the videos are played automatically, and users passively receive the recommended videos. In this new scenario, users passively express negative feedback by skipping over videos they do not like, which provides valuable information about their preferences. Different from the negative feedback studied in traditional recommender systems, this passive-negative feedback can reflect users' interests and serve as an important supervision signal in extracting users' preferences. Therefore, it is essential to carefully design and utilize it in this novel recommendation scenario. In this work, we first conduct analyses based on a large-scale real-world short-video behavior dataset and illustrate the significance of leveraging passive feedback. We then propose a novel method that deploys the sub-interest encoder, which incorporates positive feedback and passive-negative feedback as supervision signals to learn the user's current active sub-interest. Moreover, we introduce an adaptive fusion layer to integrate various sub-interests effectively. To enhance the robustness of our model, we then introduce a multi-task learning module to simultaneously optimize two kinds of feedback -- passive-negative feedback and traditional randomly-sampled negative feedback. The experiments on two large-scale datasets verify that the proposed method can significantly outperform state-of-the-art approaches. The code is released at https://github.com/tsinghua-fib-lab/RecSys2023-SINE.

翻译：序列推荐是推荐系统中最重要的任务之一，旨在以历史行为为输入推荐下一个交互物品。传统的序列推荐通常主要考虑收集到的正反馈，例如点击、购买等。然而，在TikTok等短视频平台上，视频观看行为并不总是代表正反馈。具体来说，视频会自动播放，用户被动接收推荐的视频。在这种新场景中，用户通过跳过不喜欢的视频来被动表达负反馈，这提供了关于他们偏好的有价值信息。与传统推荐系统中研究的负反馈不同，这种被动负反馈可以反映用户的兴趣，并作为提取用户偏好的重要监督信号。因此，在这一新型推荐场景中，精心设计并利用它至关重要。在本工作中，我们首先基于大规模真实世界短视频行为数据集进行分析，并说明了利用被动反馈的重要性。然后，我们提出了一种新方法，部署了子兴趣编码器，将正反馈和被动负反馈作为监督信号来学习用户当前的活跃子兴趣。此外，我们引入了一个自适应融合层来有效整合各种子兴趣。为了增强模型的鲁棒性，我们随后引入了一个多任务学习模块，同时优化两种反馈——被动负反馈和传统随机采样的负反馈。在两个大规模数据集上的实验验证了所提方法能显著超越最先进的方法。代码已在 https://github.com/tsinghua-fib-lab/RecSys2023-SINE 开源。