The 2021 SIGIR workshop on eCommerce is hosting the Coveo Data Challenge for "In-session prediction for purchase intent and recommendations". The challenge addresses the growing need for reliable predictions within the boundaries of a shopping session, as customer intentions can be different depending on the occasion. The need for efficient procedures for personalization is even clearer if we consider the e-commerce landscape more broadly: outside of giant digital retailers, the constraints of the problem are stricter, due to smaller user bases and the realization that most users are not frequently returning customers. We release a new session-based dataset including more than 30M fine-grained browsing events (product detail, add, purchase), enriched by linguistic behavior (queries made by shoppers, with items clicked and items not clicked after the query) and catalog meta-data (images, text, pricing information). On this dataset, we ask participants to showcase innovative solutions for two open problems: a recommendation task (where a model is shown some events at the start of a session, and it is asked to predict future product interactions); an intent prediction task, where a model is shown a session containing an add-to-cart event, and it is asked to predict whether the item will be bought before the end of the session.
翻译:关于电子商务的2021年SIGIR研讨会正在主办关于“购买意向和建议的会期预测”的Coveo数据挑战的Coveo数据研讨会。这项挑战解决了在购物会议范围内对可靠预测日益增长的需求,因为客户的意图可能随时间不同而不同。如果我们更广泛地考虑电子商务的格局,那么个人化的有效程序的必要性就更加明确了:除了大型数字零售商之外,由于用户基础较小以及认识到大多数用户并不经常返回客户,问题的限制更加严格。我们发布了一个新的基于届会的数据集,包括30M以上细微浏览事件(产品细节、添加、购买),由语言行为(商店家制作的餐点,点击项目和在查询后不点击的项目)和目录元数据(图像、文本、定价信息)所丰富。关于这一数据集,我们请与会者为两个公开的问题展示创新的解决办法:建议任务(在会议开始时展示了一些事件模型,并要求预测未来产品互动);意向预测任务,在会议结束前展示一个模型,在会议结束前是否购买了会议。