Elevating Seamless Housing Navigation with Precise Recommendations for Personalized Rental Experiences

Year : 2024 | Volume :11 | Issue : 03 | Page : –
By

Mohammad Shees,

Md. Faisal Quaiyum,

Saquib Hussain,

Mantasha Perween,

Hasan Nawaz,

Sunil,

  1. Student, Computer Engineering, Department of University Polytechnic, Jamia Millia Islamia, New Delhi, India
  2. Student, Computer Engineering, Department of University Polytechnic, Jamia Millia Islamia, New Delhi, India
  3. Student, Computer Engineering, Department of University Polytechnic, Jamia Millia Islamia, New Delhi, India
  4. Student, Computer Engineering, Department of University Polytechnic, Jamia Millia Islamia, New Delhi, India
  5. Student, Computer Engineering, Department of University Polytechnic, Jamia Millia Islamia, New Delhi, India
  6. Associate Professor, Faculty of Engineering & Technology, Jamia Millia Islamia, New Delhi, India

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, its aim is 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: Housing Navigation, Rental Experience, Recommendation Systems, Personalized Recommendations, Machine Learning, User Preferences, Property Data.

[This article belongs to Journal of Artificial Intelligence Research & Advances (joaira)]

How to cite this article:
Mohammad Shees, Md. Faisal Quaiyum, Saquib Hussain, Mantasha Perween, Hasan Nawaz, Sunil. Elevating Seamless Housing Navigation with Precise Recommendations for Personalized Rental Experiences. Journal of Artificial Intelligence Research & Advances. 2024; 11(03):-.
How to cite this URL:
Mohammad Shees, Md. Faisal Quaiyum, Saquib Hussain, Mantasha Perween, Hasan Nawaz, Sunil. Elevating Seamless Housing Navigation with Precise Recommendations for Personalized Rental Experiences. Journal of Artificial Intelligence Research & Advances. 2024; 11(03):-. Available from: https://journals.stmjournals.com/joaira/article=2024/view=177238

References

  1. Omkar Sheshraoji Kosare, Minal Vinod Sawarkar, Ketaki Dattaraj Sewatkar, Pratik Ladekar. Rental Property Management System. International Journal of Advanced Research in Science, Communication and Technology (IJARSCT). 2023;3(4):273.
  2. Jayadharshini, S. Santhiya, S. Keerthika, N. Abinaya, S. Priyanka. Machine learning techniques for predicting home rental prices in India. Applied and Computational Engineering. 2023. 118-124. DOI:10.54254/2755-2721/29/20231181
  3. Xian Shi and Yan Jiang. Research on house rental recommendation algorithm based on deep learning. In2022 3rd International Conference on Big Data Economy and Information Management (BDEIM 2022) 2023 Mar 29 (pp. 604-613). Atlantis Press.
  4. Mehmet Erkek, Kamil Çayırlı, Hakan Taş, Ali Hepşen and Tevfik Aytekin.: Recommenda- tion Systems Applied in Turkish Real Estate Market. Journal of Computations & Model- ling, 10, No. 1, 1-10 (2020).
  5. Shristi Simran, Akash Pande, Prof. Payal Desai.: Preference-Search based Recommenda- tion System for Accommodation Facilitator. In: 2019 International Journal of Scientific Research in Computer Science, Engineering and Information Technology (IJSRCSEIT). Volume Issue 2. ISSN: 2456-3307. DOI: https://doi.org/10.32628/CSEIT1952245 (2019).
  6. Satapathy SM, Jhaveri R, Khanna U, Dwivedi AK. Smart rent portal using recommendation system visualized by augmented reality. Procedia Computer Science. 2020 Jan 1;171:197-206.
  7. Aamir Mohammad, Mamta Bhusry.: Recommendation System: State of the Art Approach International Journal of Computer Applications (0975 – 8887). 2015;120(12):25-32.
  8. Ayush Singhal, Pradeep Sinha and Rakesh Pant.: Use of Deep Learning in Modern Rec- ommendation System: A Summary of Recent Works. International Journal of Computer 2017;180(7):17-22.
  9. Burke R. Hybrid recommender systems: Survey and experiments. User modeling and user-adapted interaction. 2002 Nov;12:331-70.
  10. Mubarak M, Tahir A, Waqar F, Haneef I, McArdle G, Bertolotto M, Saeed MT. A map-based recommendation system and house price prediction model for real estate. ISPRS International Journal of Geo-Information. 2022 Mar 7;11(3):178.
  11. Amika Mehta, Vedant Patil, Apurva : LeKeDe: Online Rental System, International Journal of Engineering Research & Technology. 2019;08(10):420-423.
  12. Ning X, Desrosiers C, Karypis G. A comprehensive survey of neighborhood-based recommendation methods. Recommender systems handbook. 2015:37-76.
  13. Singhal A, Rastogi S, Panchal N, Chauhan S, Varshney S. Research paper on recommendation system. Global Scientific Journals. 2021;9(8):935-940.
  14. Roy D, Dutta M. A systematic review and research perspective on recommender systems. Journal of Big Data. 2022 May 3;9(1):59.
  15. Linyi Wang. Commerce Product Recommendation Algorithm Based on Collaborative Filtering. Intelligent Computing Technology and Automation. 2024. 617-624. DOI:3233/ATDE231237
  16. Melyani CA. Hotel recommendation system with content-based filtering approach (case study: Hotel in yogyakarta on nusatrip website). J Statistika: Jurnal Ilmiah Teori dan Aplikasi Statistika. 2022 Jul 30;15(1):152-157.
  17. Zhang J, Bian Z, Wang S. Bayes‐Decisive Linear KNN with Adaptive Nearest Neighbors. International Journal of Intelligent Systems. 2024;2024(1):6664942.
  18. Buradkar K, Kori S, Ruikar S, Galfat V, Patil D, Nasare R. Property Rental Management System. International Journal of Computer Science and Mobile Computing. 2022;11(11):177-179.
Regular Issue Subscription Review Article
Volume 11
Issue 03
Received 18/05/2024
Accepted 13/09/2024
Published 07/10/2024
Views: 14

Check Our other Platform for Workshops in the field of AI, Biotechnology & Nanotechnology.
Learn more
Check Out Platform for Webinars in the field of AI, Biotech. & Nanotech.
Learn more