We propose an end-to-end real-estate recommendation system, RE-RecSys, which has been productionized in real-world industry setting. We categorize any user into 4 categories based on available historical data: i) cold-start users; ii) short-term users; iii) long-term users; and iv) short-long term users. For cold-start users, we propose a novel rule-based engine that is based on the popularity of locality and user preferences. For short-term users, we propose to use content-filtering model which recommends properties based on recent interactions of users. For long-term and short-long term users, we propose a novel combination of content and collaborative filtering based approach which can be easily productionized in the real-world scenario. Moreover, based on the conversion rate, we have designed a novel weighing scheme for different impressions done by users on the platform for the training of content and collaborative models. Finally, we show the efficiency of the proposed pipeline, RE-RecSys, on a real-world property and clickstream dataset collected from leading real-estate platform in India. We show that the proposed pipeline is deployable in real-world scenario with an average latency of <40 ms serving 1000 rpm.

翻译：我们提出了一种端到端的房地产推荐系统RE-RecSys，该系统已在真实工业场景中投入生产。基于可用历史数据，我们将用户分为四类：i)冷启动用户；ii)短期用户；iii)长期用户；以及iv)短期-长期用户。针对冷启动用户，我们提出了一种基于区域流行度与用户偏好相结合的新型规则引擎。针对短期用户，我们提出采用基于用户近期交互行为的内容过滤推荐模型。对于长期用户及短期-长期用户，我们提出了一种结合内容过滤与协同过滤的创新方法，该方法可轻松部署于真实场景。此外，基于转化率指标，我们设计了一种新颖的权重分配方案，用于训练内容模型与协同模型时对用户平台上的不同交互行为进行加权。最后，我们基于印度领先房地产平台收集的真实房产与点击流数据集验证了所提管道RE-RecSys的有效性。实验表明，该管道可在真实场景中部署，在服务1000转/分钟时平均延迟小于40毫秒。