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.

我们提出了一个实时房地产推荐系统，RE-RecSys，它已在实际产业环境中实施。我们基于可用的历史数据将用户分为四个类别：i）冷启动用户；ii）短期用户；iii）长期用户；和 iv）短长期用户。对于冷启动用户，我们提出了一种基于地区热度和用户偏好的新型规则引擎。对于短期用户，我们提出使用基于内容过滤模型的推荐属性，该模型根据用户最近的互动进行推荐。对于长期和短长期用户，我们提出了一种结合内容和协作过滤的新颖方法，可以轻松实施于实际情景。此外，根据转化率，我们为平台上用户执行的不同印象设计了一种新颖的加权方案，用于内容和协作模型的训练。最后，我们展示了来自印度领先房地产平台的实际房产和点击流数据集上所提出的管道 RE-RecSys 的效率。我们表明，所提出的管道可以在实际情景中部署，平均延迟小于 40 毫秒，每分钟处理 1000 次请求。