Buy It Again (BIA) recommendations are crucial to retailers to help improve user experience and site engagement by suggesting items that customers are likely to buy again based on their own repeat purchasing patterns. Most existing BIA studies analyze guests personalized behavior at item granularity. A category-based model may be more appropriate in such scenarios. We propose a recommendation system called a hierarchical PCIC model that consists of a personalized category model (PC model) and a personalized item model within categories (IC model). PC model generates a personalized list of categories that customers are likely to purchase again. IC model ranks items within categories that guests are likely to consume within a category. The hierarchical PCIC model captures the general consumption rate of products using survival models. Trends in consumption are captured using time series models. Features derived from these models are used in training a category-grained neural network. We compare PCIC to twelve existing baselines on four standard open datasets. PCIC improves NDCG up to 16 percent while improving recall by around 2 percent. We were able to scale and train (over 8 hours) PCIC on a large dataset of 100M guests and 3M items where repeat categories of a guest out number repeat items. PCIC was deployed and AB tested on the site of a major retailer, leading to significant gains in guest engagement.

翻译：再次购买（BIA）推荐对于零售商至关重要，它通过基于顾客自身的重复购买模式推荐其可能再次购买的商品，有助于提升用户体验和网站互动。现有大多数BIA研究在商品粒度上分析顾客的个性化行为，而在某些场景下，基于品类的模型可能更为合适。我们提出一种名为分层PCIC模型的推荐系统，该模型由个性化品类模型（PC模型）和品类内个性化商品模型（IC模型）组成。PC模型生成顾客可能再次购买的个性化品类列表，而IC模型则对顾客可能在某个品类中消费的商品进行排序。分层PCIC模型利用生存模型捕获产品的总体消费速率，并使用时间序列模型捕捉消费趋势。从这些模型中提取的特征被用于训练一个基于品类粒度的神经网络。我们在四个标准开放数据集上将PCIC与十二种现有基线方法进行比较。PCIC在召回率提升约2%的同时，将NDCG最高提升了16%。我们能够在包含1亿顾客和300万商品的大规模数据集上（训练时间超过8小时）对PCIC进行扩展和训练，其中顾客的重复品类数量远多于重复商品。PCIC已在一家大型零售商的网站上部署并进行A/B测试，显著提升了顾客互动率。