Development of a Hybrid Real Estate Recommender System

Hakan Tas, Hilal Erdogan Sumnu, Bahadir Gokoz and Tevfik Aytekin
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Hakan Tas: Zingat.com, Istanbul, Turkey
Hilal Erdogan Sumnu: Zingat.com, Istanbul, Turkey
Bahadir Gokoz: Zingat.com, Istanbul, Turkey
Tevfik Aytekin: Bahcesehir University, Istanbul, Turkey

International Journal of Technology and Engineering Studies, 2019, vol. 5, issue 3, 90-94

Abstract: Today consumers are confronted with a very large number of products and services to choose from. This makes it difficult for users to find relevant products among a huge number of alternatives. Recommender systems help users to find products of interest by analyzing past user transactions such as product views and purchases. There is a similar problem in the real estate industry where hundreds of thousands of properties are available for rentals or sales. In this work we present the details of a real estate recommender system developed for Zingat.com. The system developed is a hybrid of collaborative and content filtering approaches. We will explain the challenges we face, the recommendation techniques we use to overcome these challenges, and the final product used for recommendation.

Keywords: Recommendation engine; artificial intelligence; machine learning; real estate (search for similar items in EconPapers)
Date: 2019
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Persistent link: https://EconPapers.repec.org/RePEc:apa:ijtess:2019:p:90-94

DOI: 10.20469/ijtes.5.10003-3

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