STM Journals Peer-reviewed research from Consortium e-Learning Network

Journal of Artificial Intelligence Research & Advances Review Article

Elevating Seamless Housing Navigation with Precise Recommendations for Personalized Rental Experiences

  1. Mohammad Shees Computer Engineering, Department of University Polytechnic, Jamia Millia Islamia
  2. Md. Faisal Quaiyum Computer Engineering, Department of University Polytechnic, Jamia Millia Islamia
  3. Saquib Hussain Computer Engineering, Department of University Polytechnic, Jamia Millia Islamia
  4. Mantasha Perween Computer Engineering, Department of University Polytechnic, Jamia Millia Islamia
  5. Hasan Nawaz Computer Engineering, Department of University Polytechnic, Jamia Millia Islamia
  6. Sunil Faculty of Engineering and Technology, Jamia Millia Islamia

Abstract

The demand for rental housing has increased significantly in recent years, making more effective and individualized methods of housing navigation necessary. Renters become frustrated and ineffective when using traditional ways of researching rental houses since they frequently involve manual searches and little customization. By using sophisticated recommendation systems to deliver exact recommendations catered to user preferences, this research article suggests a novel way to improve house navigation. The proposed approach seeks to optimize rental property utilization, improve user satisfaction, and streamline the rental experience by merging machine learning algorithms with property data and user preferences. The primary objective is to introduce an advanced recommendation system that not only simplifies the process but also adapts to the dynamic preferences of users. By integrating a preference-based search technique with cutting-edge example-critiquing methodology, it aims to redefine the user experience in the realm of online property discovery. This forward-looking approach not only enhances efficiency but also sets a new standard for tailored and responsive solutions in the dynamic landscape of online property discovery. The paper investigates the difficulties in housing navigation, the possible advantages of personalized recommendations, and the technological issues involved in creating such systems through a thorough analysis of the body of existing material. It also addresses the privacy issues and ethical ramifications of individualized advice. The paper concludes with a framework for deploying accurate recommendation systems in the rental housing market, highlighting the significance of algorithmic fairness, user control, and transparency.

Keywords

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