PERSONALISED RECOMMENDATION SYSTEM FOR E-COMMERCE

COMPUTER ENGINEERING

In this era of rapid technological advancements, personalized recommendation systems powered by AI are gaining prominence. Such systems leverage machine learning algorithms to analyse user preferences, behaviour, and historical data to provide tailored recommendations in an E-Commerce website. By considering factors like user demographics, past purchases, reviews, and specifications (keywords), these systems aim to deliver accurate and relevant suggestions to individual users. The recommendation process typically involves several steps. Firstly, user data is collected, including demographic information, browsing history, and previous purchases. Collaborative filtering techniques compare a user's preferences with those of other similar users, recommending products that have been liked or purchased by users with comparable tastes. Additionally, explicit feedback such as ratings and reviews may be incorporated. Next, the system utilizes various AI techniques like collaborative filtering, content-based filtering, or hybrid approaches to process and analyse this data. To enhance the personalization aspect, AI models can be trained to adapt to individual user behaviour over time of period. Privacy and data security are critical considerations in personalized recommendation systems. User consent and anonymization techniques are employed to protect personal data and ensure compliance with data protection regulations. The ultimate goal of an AI-based personalized recommendation system for E-Commerce is to simplify the decision-making process for users and provide them with a curated list of options that best match their preference sand needs. By leveraging ML algorithms, these systems strive to enhance user satisfaction, increase customer engagement, and improve the overall shopping experience in the E-commerce website.

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