An Intelligent Recommendation System for Real Estate Commodity

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-toconsumer 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.

Profitability and financial performance of Italian real estate companies: quantitative profiles

“…The challenge in the future lies above all in financial dynamics, as well as in production techniques, requiring more outstanding qualifications consultants and appraisers (Tsung, 2022) who will have to develop new skills capable of monitoring the entire technical and financial production process (Baiardi and Tronconi, 2010).…”

Online Course Recommendation Using Deep Convolutional Neural Network with Negative Sequence Mining

“…Collaborative filtering-based course recommendation focuses on mining the similarity of learners (i.e., users) and courses, with the basic assumption that similar users would take similar courses, but it can only make recommendations based on users with similar behaviours with target users, failing to solve the cold start problems, such as recommendation for new registered users [21,22]. Hybrid recommendation is a combination approach by mixing content and collaborative filteringbased methods to help solve the problems of missing recommendation values and cold start [23][24][25]. Sequence-based approaches focus on mining learning behaviour sequences of users.…”

“…Sequence-based approaches focus on mining learning behaviour sequences of users. SB methods are further mainly divided into two categories: the first one is to update data through sequences, and the other is to model learning behaviour sequences using time series technology [25].…”