RE-RecSys: An End-to-End system for recommending properties in Real-Estate domain
<|reference_start|>RE-RecSys: An End-to-End system for recommending properties in Real-Estate domain: 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.<|reference_end|>
@article{c2024re-recsys:,
    title={RE-RecSys: An End-to-End system for recommending properties in
  Real-Estate domain},
    author={Venkatesh C, Harshit Oberoi, Anil Goyal, Nikhil Sikka},
    journal={arXiv preprint arXiv:2404.16553},
    year={2024},
    doi={10.1145/3632410.3632487},
    archivePrefix={arXiv},
    eprint={2404.16553},
    primaryClass={cs.IR cs.LG}
}