Feed recommendation systems, which recommend a sequence of items for users to browse and interact with, have gained significant popularity in practical applications. In feed products, users tend to browse a large number of items in succession, so the previously viewed items have a significant impact on users' behavior towards the following items. Therefore, traditional methods that mainly focus on improving the accuracy of recommended items are suboptimal for feed recommendations because they may recommend highly similar items. For feed recommendation, it is crucial to consider both the accuracy and diversity of the recommended item sequences in order to satisfy users' evolving interest when consecutively viewing items. To this end, this work proposes a general re-ranking framework named Multi-factor Sequential Re-ranking with Perception-Aware Diversification (MPAD) to jointly optimize accuracy and diversity for feed recommendation in a sequential manner. Specifically, MPAD first extracts users' different scales of interests from their behavior sequences through graph clustering-based aggregations. Then, MPAD proposes two sub-models to respectively evaluate the accuracy and diversity of a given item by capturing users' evolving interest due to the ever-changing context and users' personal perception of diversity from an item sequence perspective. This is consistent with the browsing nature of the feed scenario. Finally, MPAD generates the return list by sequentially selecting optimal items from the candidate set to maximize the joint benefits of accuracy and diversity of the entire list. MPAD has been implemented in Taobao's homepage feed to serve the main traffic and provide services to recommend billions of items to hundreds of millions of users every day.

翻译：前馈推荐系统为用户推荐一系列可浏览和互动的项目，在现实应用中日益普及。在信息流产品中，用户倾向于连续浏览大量内容，因此先前浏览的项目会显著影响用户对后续项目的交互行为。传统方法主要致力于提升推荐项目的准确性，但由于可能推荐高度相似的项目，这类方法在信息流推荐场景中表现欠佳。针对信息流推荐，为满足用户连续浏览时不断变化的兴趣，必须同时考虑推荐序列的准确性与多样性。为此，本文提出一种通用重排序框架——多因子序列重排结合感知感知多样化（MPAD），通过序列化方式联合优化信息流推荐的准确性与多样性。具体而言，MPAD首先基于图聚类聚合方法从用户行为序列中提取不同粒度的兴趣特征；继而设计两个子模型，通过分别捕捉用户因动态上下文产生的兴趣演变以及用户从序列视角对多样性的个人感知，来评估特定项目的准确性与多样性。这种设计契合信息流场景的浏览特性。最终，MPAD通过从候选集中顺序选择最优项目生成返回列表，以实现整个列表准确性与多样性的联合收益最大化。该框架已部署于淘宝首页信息流，支撑核心流量分发，每日为亿万级用户推荐数十亿级商品。