Many current recommender systems mainly focus on the product-to-product recommendations and user-to-product recommendations even during the time of events rather than modeling the typical recommendations for the target event (e.g., festivals, seasonal activities, or social activities) without addressing the multiple aspects of the shopping demands for the target event. Product recommendations for the multiple aspects of the target event are usually generated by human curators who manually identify the aspects and select a list of aspect-related products (i.e., product carousel) for each aspect as recommendations. However, building a recommender system with machine learning is non-trivial due to the lack of both the ground truth of event-related aspects and the aspect-related products. To fill this gap, we define the novel problem as the event-based product carousel recommendations in e-commerce and propose an effective recommender system based on the query-click bipartite graph. We apply the iterative clustering algorithm over the query-click bipartite graph and infer the event-related aspects by the clusters of queries. The aspect-related recommendations are powered by the click-through rate of products regarding each aspect. We show through experiments that this approach effectively mines product carousels for the target event.

翻译：当前许多推荐系统主要聚焦于产品与产品间的推荐以及用户与产品间的推荐，即便在事件周期内也是如此，而非针对目标事件（例如节日、季节性活动或社交活动）建模典型推荐，未能解决目标事件购物需求的多元维度。针对目标事件多维度需求的产品推荐通常由人工策展人完成，他们手动识别维度并为每个维度选择一系列与该维度相关的产品（即产品轮播）作为推荐。然而，由于缺乏事件相关维度的真实标注以及维度相关产品数据，基于机器学习构建推荐系统面临显著挑战。为填补这一空白，我们定义了电子商务中基于事件的产品轮播推荐这一新问题，并提出一种基于查询-点击二分图的高效推荐系统。我们利用迭代聚类算法处理查询-点击二分图，通过查询聚类推断事件相关维度，并基于各维度下产品的点击率实现维度相关推荐。实验表明，该方法能够有效挖掘目标事件的产品轮播。