Open Access

ARTICLE

An Intelligent Recommendation System for Real Estate Commodity

by Tsung-Yin Ou¹, Guan-Yu Lin², Hsin-Pin Fu¹, Shih-Chia Wei¹, Wen-Lung Tsai^3,*

1 Department of Marketing and Distribution Management, National Kaohsiung University of Science and Technology, Kaohsiung, 824, Taiwan
2 College of Management, National Kaohsiung University of Science and Technology, Kaohsiung, 824, Taiwan
3 Department of Information Management, Asia Eastern University of Science and Technology, New Taipei, 220, Taiwan

* Corresponding Author: Wen-Lung Tsai. Email:

(This article belongs to the Special Issue: Soft Computing and Big Data Mining)

Computer Systems Science and Engineering 2022, 42(3), 881-897. https://doi.org/10.32604/csse.2022.022637

Received 13 August 2021; Accepted 30 September 2021; Issue published 08 February 2022

Abstract

Most real estate agents develop new objects by visiting unfamiliar clients, distributing leaflets, or browsing other real estate trading website platforms, whereas consumers often rely on websites to search and compare prices when purchasing real property. In addition to being time consuming, this search process renders it difficult for agents and consumers to understand the status changes of objects. In this study, Python is used to write web crawler and image recognition programs to capture object information from the web pages of real estate agents; perform data screening, arranging, and cleaning; compare the text of real estate object information; as well as integrate and use the convolutional neural network of a deep learning algorithm to implement image recognition. In this study, data are acquired from two business-to-consumer real estate agency networks, i.e., the Sinyi real estate agent and the Yungching real estate agent, and one consumer-to-consumer real estate agency platform, i.e., the, FiveNineOne real estate agent. The results indicate that text mining can reveal the similarities and differences between the objects, list the number of days that the object has been available for sale on the website, and provide the price fluctuations and fluctuation times during the sales period. In addition, 213,325 object amplification images are used as a database for training using deep learning algorithms, and the maximum image recognition accuracy achieved is 95%. The dynamic recommendation system for real estate objects constructed by combining text mining and image recognition systems enables developers in the real estate industry to understand the differences between their commodities and other businesses in approximately 2 min, as well as rapidly determine developable objects via comparison results provided by the system. Meanwhile, consumers require less time in searching and comparing prices after they have understood the commodity dynamic information, thereby allowing them to use the most efficient approach to purchase real estate objects of their interest.

---

Keywords

---