Problem statement
Online shoppers are inundated with a vast array of product choices, leading to decision fatigue and reduced satisfaction. Traditional recommendation systems often lack personalization and fail to adapt to individual user preferences and behaviors, resulting in suboptimal user experiences and missed sales opportunities for e-commerce platforms.
Abstract
Create an AI-powered product recommendation engine that leverages machine learning algorithms to analyze user behavior, preferences, and historical data. The system should dynamically adapt to changing user interests and deliver highly personalized product suggestions in real-time, aiming to enhance user satisfaction, increase engagement, and boost sales for e-commerce platforms.
Outcome
The developed recommendation engine will significantly improve the user shopping experience by providing accurate and personalized product suggestions. This, in turn, is expected to increase user engagement, retention, and conversion rates for e-commerce platforms. The AI-driven system will continuously learn and refine its recommendations, ensuring that users are presented with products that align with their evolving preferences, ultimately maximizing customer satisfaction and business revenue.
Reference
The most valuable artificial intelligence application for e-commerce to social media websites these days is a smart recommendation engine that can filter panoply of information on the internet and recommend personalized products and services to each user. An efficient and reliable recommender engine (RE) increases sells and profit of the e-commerce websites, thus its performance is very crucial. Traditional RE suffers from cold-start, low accuracy, and scalability to Big Data problem. Thus, RE research has started again with great enthusiasm to explore newer techniques with deep learning artificial neural nets, as more computing power in parallel processing framework become available from latter half of this decade. In recent years deep learning (DL) artificial neural nets (ANN) have given breakthrough performance in areas like image processing and natural language processing tasks. So, its usability needs to be researched for recommender engine design also. This paper first explores the traditional ways of making a recommender engine and then evaluates the use of deep learning neural net techniques.
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