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.
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This research was partly supported by HEC Grant TDF-029.
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Rehman, F., Masood, H., Ul-Hasan, A., Nawaz, R., Shafait, F. (2020). An Intelligent Context Aware Recommender System for Real-Estate. In: Djeddi, C., Jamil, A., Siddiqi, I. (eds) Pattern Recognition and Artificial Intelligence. MedPRAI 2019. Communications in Computer and Information Science, vol 1144. Springer, Cham. https://doi.org/10.1007/978-3-030-37548-5_14
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