A Content CF Location based Recommendation system and Price Prediction with Zameen.com
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Date
2023
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Publisher
UMT, Lahore
Abstract
Global real estate is one of the primary contributors to the economic prosperity and stability of any nation. In 2015, it had a value of $217 trillion, or roughly 2.7 times the world's GDP. It also represents approximately 60% of the world's total conventional resources on the globe. Real estate investors will be able to make better decisions and generate more revenue because of the availability of big data. By assessing user inclinations and preferences, which can then be retained or captured while a user engages in certain activities on zameen.com, customization can aid in the formation of judgments. A personalized real estate portal can use this information to recommend properties, assist homeowners, and provide informative real estate statistics. In this article, the foundation for recommending properties to consumers is presented. By monitoring user interactions on an online real estate site, the framework may deliver customized real estate recommendations based on content, cooperation, and region. The user feedback mechanism examined the usefulness of the recommendations using a hit ratio metric, and the findings indicate that 70% accurate suggestions were given, which indicates that customers were interested in at least three of the five options that were offered.