While personalised recommendations are successful in domains like retail, where large volumes of user feedback on items are available, the generation of automatic recommendations in data-sparse domains, like insurance purchasing, is an open problem. The insurance domain is notoriously data-sparse because the number of products is typically low (compared to retail) and they are usually purchased to last for a long time. Also, many users still prefer the telephone over the web for purchasing products, reducing the amount of web-logged user interactions. To address this, we present a recurrent neural network recommendation model that uses past user sessions as signals for learning recommendations. Learning from past user sessions allows dealing with the data scarcity of the insurance domain. Specifically, our model learns from several types of user actions that are not always associated with items, and unlike all prior session-based recommendation models, it models relationships between input sessions and a target action (purchasing insurance) that does not take place within the input sessions. Evaluation on a real-world dataset from the insurance domain (ca. 44K users, 16 items, 54K purchases, and 117K sessions) against several state-of-the-art baselines shows that our model outperforms the baselines notably. Ablation analysis shows that this is mainly due to the learning of dependencies across sessions in our model. We contribute the first ever session-based model for insurance recommendation, and make available our dataset to the research community.
翻译:虽然个人化建议在零售等有大量用户对项目的反馈的领域中很成功,但在诸如保险采购等数据偏差领域生成自动建议是一个公开的问题。保险领域由于产品数量通常较低(与零售相比),而且通常购买时间长,因此数据偏差臭名昭著。此外,许多用户仍然偏好电话而不是网络购买产品,减少网上用户互动的数量。为此,我们提出了一个经常性的神经网络建议模式,利用过去用户会议作为学习建议的信号。从过去的用户会议上学习,可以处理保险领域的数据稀缺问题。具体地说,我们的模式从一些并非总与项目相关的类型的用户行动中学习数据。不同于以往的会议建议模式,它模拟了投入会议与目标行动(采购保险)之间的关系,而不是在投入会议中进行,减少了网上用户互动的数量。为了解决这个问题,我们提出了一个经常使用的神经网络建议模型集(例如44K用户、16个项目、54K采购和117K会议),相对于若干州级用户会议的学习,可以处理保险领域的数据稀缺问题。具体地说,我们的模型展示了我们第一次学习的基线。