SwapRec：通过训练时的交换为冷启动项目预热
SwapRec: Warming Up Cold Items Through Training-Time Swaps
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中文总结 AI 辅助
针对序列推荐模型对冷启动项目交换操作鲁棒性不足的问题，提出SwapRec方法，通过在训练时采用推理时的交换启发式，在三个领域的实验中提升了含冷启动项目交互时的推荐准确性及冷启动项目占比。
中文摘要 AI 辅助
与冷启动项目的交互会对基于ID的推荐系统的实时个性化推荐产生负面影响，原因在于这类交互会降低用户偏好估计的准确性，而将冷启动项目排除在用户画像之外则会阻碍实时推荐的更新。在工业场景中，一种常用的启发式方法是在推理时将冷启动项目替换为与其最相似的“热启动”邻居（相似性由项目的侧信息推断得出），以此解决该问题。本文证明，通常用于实时个性化推荐的序列模型对这类交换操作并不鲁棒，并提出了SwapRec方法来解决这一问题。SwapRec的核心是在训练时也采用相同的交换启发式方法。我们将SwapRec应用于序列推荐领域的最先进模型，并在三个推荐领域（在线购物、电影、音乐）中通过定量实验分析其效果。实验结果表明，无论底层序列架构如何，易于实现的SwapRec方法在存在冷启动项目交互时能显著提升推荐的准确性，同时还能增加推荐列表中冷启动项目的占比。
英文摘要
Interactions with cold items negatively impact real-time personalization of ID-based recommender systems. This is because the use of such interactions degrades user preference estimates, whereas excluding cold items from the user profile prevents real-time recommendation updates. In industrial scenarios, one heuristic often applied to address this shortcoming at inference time is to replace, i.e., "swap", cold-start items by their most similar "warm" neighbor, where similarity is inferred from the items' side information. In this paper, we demonstrate that sequential models, most often used for real-time personalization, are not robust to such swaps, and propose SwapRec, an approach to address this issue. SwapRec relies on using the same swap heuristics already at training time. We apply SwapRec to state-of-the-art models for sequential recommendation and analyze its impact by means of quantitative experiments in three recommendation domains (online shopping, movie, music). The experimental results show that, irrespective of the underlying sequential architecture, our easy-to-implement SwapRec approach allows for substantially more accurate recommendations when in presence of interactions with cold items, simultaneously leading to a larger percentage of cold items in the recommendation lists.
