Trending Doors

Authors: Bansode D.K, Sangle Kalyani Sachin, Shahane Sakshi Bandu, Shirsath Gayatri Subhash, Shinde Vidhya Namdev

Country: India

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Abstract: The proposed system, "Trending Doors", leverages machine learning to enhance the shopping experience by providing a personalized platform for discovering the latest offers, discounts, and deals from various malls across the city. Using advanced recommendation algorithms, the system analyses user preferences, shopping behaviour, and trending deals to suggest the most relevant promotions and discounts. Users can browse through a curated selection of products, compare discounts from different malls, and add their desired items to a virtual shopping cart. This personalized shopping platform not only saves users time but also ensures they make informed purchasing decisions. Once users finalize their shopping list, they can visit the mall in person to purchase the products offline, bridging the convenience of online planning with the tactile experience of offline shopping. The integration of machine learning algorithms helps the system dynamically adapt to user preferences and shopping trends, ensuring a tailored and efficient shopping journey. Additionally, the platform benefits malls and retailers by optimizing the visibility of their offers and deals, attracting more foot traffic, and fostering a seamless connection between shoppers and retailers.

Keywords: Trending Doors, machine learning, shopping experience, personalized platform, offers, discounts, deals, malls, recommendation algorithms, user preferences, shopping behaviour.

Paper Id: 232031

Published On: 2025-01-16

Published In: Volume 13, Issue 1, January-February 2025

Cite This: Trending Doors - Bansode D.K, Sangle Kalyani Sachin, Shahane Sakshi Bandu, Shirsath Gayatri Subhash, Shinde Vidhya Namdev - IJIRMPS Volume 13, Issue 1, January-February 2025.

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