AI BASED PERSONALISED ELECTRONIC GADGETS RECOMMENDATION SYSTEM
AKIL ADHARSH N, INDRAKUMAR R S, HARISH B ,BALASAMY K
ABSTRACT
In this era of rapid technological advancements, personalized electronic gadg et recommendation systems powered by ai are gaining prominence. such syste ms leverage machine learning algorithms to analyse user preferences, behavio ur, and historical data to provide tailored recommendations for electronic gad gets. by considering factors like user demographics, past purchases, reviews, a nd specifications of gadgets, these systems aim to deliver accurate and relevan t suggestions to individual users. the recommendation process typically involv es several steps. firstly, user data is collected, including demographic informa tion, browsing history, and previous purchases. collaborative filtering techniq ues compare a user's preferences with those of other similar users, recommen ding gadgets that have been liked or purchased by users with comparable tast es. additionally, explicit feedback such as ratings and reviews may be incorpo rated. next, the system utilizes various ai techniques like collaborative filtering
, content-based filtering, or hybrid approaches to process and analye this data
.to enhance the personalization aspect, ai models can be trained to adapt to in dividual user behaviour over time of period. privacy and data security are crit ical considerations in personalized recommendation systems. user consent an d anonymization techniques are employed to protect personal data and ensure compliance with data protection regulations. the ultimate goal of an ai-based personalized electronic gadget recommendation system is to simplify the decis ion-making process for users and provide them with a curated list of options t hat best match their preferences and needs. by leveraging ai algorithms, these systems strive to enhance user satisfaction, increase customer engagement, an d improve the overall shopping experience in the electronic gadget domain.
KEYWORDS: Recommendation system, Similarity, personalization, content-based filtering, hybrid recommendation
