Manchester Metropolitan University's Research Repository

DeepRank: Adapting Neural Tensor Networks for Ranking the Recommendations

Kabir, RH, Pervaiz, B, Khan, TM, Ul-Hasan, A, Nawaz, Raheel ORCID logoORCID: https://orcid.org/0000-0001-9588-0052 and Shafait, F (2019) DeepRank: Adapting Neural Tensor Networks for Ranking the Recommendations. In: 3rd Mediterranean Conference on Pattern Recognition and Artificial Intelligence, MedPRAI 2019, 22 December 2019 - 23 December 2019, Istanbul, Turkey.


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Online real estate property portals are gaining great attraction from masses due to ease in finding properties for rental or sale/purchase. With a few clicks, a real estate portal can display relevant information to a user by ranking the searched items according to user’s specifications. It is highly significant that the ranking results display the most relevant search results to the user. Therefore, an efficient ranking algorithm that takes user’s context is crucial for enhancing user experience in finding real estate properties online. This paper proposes an expressive Neural Tensor Network to rank the properties when searched for based on the similarity between the two property entities. Previous similarity techniques do not take into account the numerous complex features used to define a property. We showed that the performance can be enhanced if the property entities are represented as an average of their constituting features before finding the similarity between them. The proposed method takes into account each feature dynamically and ranks properties according to similarity with an accuracy of 86.6%.

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