2019
DOI: 10.1007/978-3-030-37548-5_14
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An Intelligent Context Aware Recommender System for Real-Estate

Abstract: Finding products and items in large online space that meet the user needs is difficult. Users may spend a considerable amount of time before finding item relevant to their needs. Like many other economic branches, growing Internet usage also change user behavior in the real-estate market. Advancement in virtual reality offers a virtual tour, interactive maps, floor plans that make an online rental website popular among users. With an abundance of information, recommender systems become more than ever important… Show more

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Cited by 8 publications
(9 citation statements)
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References 17 publications
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“…Collaborative filtering [19][20][21][22][23][24][25] Content-based filtering [26][27][28][29] Knowledge-based [18,[30][31][32]] Multi criteria decision making [32][33][34][35][36] Reinforcement learning [37,38] Hybrid approach [39,40] Other approaches [34,41,42] 4.1. Collaborative Filtering CF is widely used as an effective recommendation approach in various applications.…”
Section: Model Studiesmentioning
confidence: 99%
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“…Collaborative filtering [19][20][21][22][23][24][25] Content-based filtering [26][27][28][29] Knowledge-based [18,[30][31][32]] Multi criteria decision making [32][33][34][35][36] Reinforcement learning [37,38] Hybrid approach [39,40] Other approaches [34,41,42] 4.1. Collaborative Filtering CF is widely used as an effective recommendation approach in various applications.…”
Section: Model Studiesmentioning
confidence: 99%
“…Rehman et al [20] cast the real estate recommendation problem as a session-based recommendation task where the RS should predict the next item of a session given the previous items in the session. They specifically proposed a two-step recommendation task.…”
Section: Model-based Collaborative Filteringmentioning
confidence: 99%
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