Manchester Metropolitan University logo
e-space
Manchester Metropolitan University's Research Repository

    An Intelligent Context Aware Recommender System for Real-Estate

    Rehman, F, Masood, H, Ul-Hasan, A, Nawaz, Raheel ORCID logoORCID: https://orcid.org/0000-0001-9588-0052 and Shafait, F (2019) An Intelligent Context Aware Recommender System for Real-Estate. In: 3rd Mediterranean Conference on Pattern Recognition and Artificial Intelligence, MedPrai 2019, 22 December 2019 - 23 December 2019, Istanbul, Turkey.

    [img]
    Preview
    		 Accepted Version
    Available under License In Copyright.
    Download (491kB) | Preview

    Abstract

    Finding products and items in large online space that meet user needs is difficult. Time spent searching before finding a relevant item can be a significant time sink for users. As with other economic branches, growing Internet usage also changed user behavior in the real-estate market. Advancements in virtual reality offer virtual tours and interactive map and floor plans which make an online rental websites very popular among users. With the abundance of information, recommender systems become more important than ever to give the user relevant property suggestions and reduce search time. A sophisticated recommender in this domain can help reduce the need of a real-estate agent. Session-based user behavior and lack of user profiles leads to the use of traditional recommendation methods. In this research, we propose an approach for real-estate recommendation based on Gated Orthogonal Recurrent Unit (GORU) and Weighted Cosine Similarity. GORU captures the user search context and weighted cosine similarity improves the rank of pertinent property. We have used the data of an online public real estate web portal (AARZ.PK). The data represents the original behavior of the user on an online portal. We have used Recall, User coverage and Mean Reciprocal Rank (MRR) metrics for the evaluation of our system against other state-of-the-art techniques. The proposed solution outperforms various baselines and state-of-the-art RNN based solutions.

    Item Type: Conference or Workshop Item (Paper)
    Published Proceedings: Communications in Computer and Information Science 2019
    Volume: 1144 C
    Peer-reviewed: Yes
    Date Deposited: 24 Feb 2020 15:36
    Publisher: Springer
    Additional Information: 
    Error parsing XML in render_xhtml_field: :1: parser error : attributes construct error
on is available online at <a href="https://doi.org/10.1007/978-3-030-37548-5_14"
                                                                                ^
:1: parser error : Couldn't find end of Start Tag a line 1
on is available online at <a href="https://doi.org/10.1007/978-3-030-37548-5_14"
                                                                                ^
:1: parser error : Opening and ending tag mismatch: fragment line 1 and a
10.1007/978-3-030-37548-5_14"">https://doi.org/10.1007/978-3-030-37548-5_14"</a>
                                                                                ^
:1: parser error : Extra content at the end of the document
10.1007/978-3-030-37548-5_14"">https://doi.org/10.1007/978-3-030-37548-5_14"</a>
                                                                                ^
    Divisions: Faculties > Business and Law
    URI: https://e-space.mmu.ac.uk/id/eprint/625002
    DOI: https://doi.org/10.1007/978-3-030-37548-5_14
    ISSN 1865-0929

    Impact and Reach

    Statistics

    DownloadsShow export options
    Activity Overview
    6 month trend
    697Downloads
    6 month trend
    227Hits

    Additional statistics for this dataset are available via IRStats2.

    Altmetric

    Repository staff only

    Edit record Edit record