Pairwise learning to recommend with both users’ and items’ contextual information

Huang, Zhenhua; Shijia, E; Zhang, Jiawen; Zhang, Bo; Ji, Zilian

doi:10.1049/iet-com.2016.0112

Cited by 14 publications

(1 citation statement)

References 23 publications

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“…Recommendation systems are primarily used to recommend commodities that may be of interest to users; they benefit not only users, but also businesses by increasing sales [22]. The services or products that are mostly likely to be of interest to consumers are displayed through the recommendation system [23].…”

Section: Recommendation Systemsmentioning

confidence: 99%

An Intelligent Recommendation System for Real Estate Commodity

Ou¹,

Lin²,

Fu³

et al. 2022

Computer Systems Science and Engineering

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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.

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Section: Recommendation Systemsmentioning

confidence: 99%