  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.
