# RE-RecSys: An End-to-End system for recommending properties in Real-Estate domain

arXiv: https://arxiv.org/abs/2404.16553 · HTML: https://mlsanity.com/paper/2404.16553/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 10 references that are in the map.
- First posted on arXiv 25 Apr 2024.

## Builds on (most cited first, 1 of 1)

- [Matrix Completion and Low-Rank SVD via Fast Alternating Least Squares](https://mlsanity.com/paper/1410.2596/) (2014)

## Built on by (most cited first, 2 of 2)

- [Model-agnostic post-hoc explainability for recommender systems](https://mlsanity.com/paper/2509.10245/) (2025)
- [HabitatAgent: An End-to-End Multi-Agent System for Housing Consultation](https://mlsanity.com/paper/2604.00556/) (2026)

## Abstract (arXiv)

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.