Social Media Based Recommender System for E-Commerce Platforms


When social media networks first began to appear just over a decade ago, no one expected these new types of forums to take off as quickly as they did. Social networking has developed into something more than merely a means for friends to communicate with one another over time. Businesses have long recognized the benefits of social media and have used it to their advantage, opening the way for others to follow. Before social media, companies had to attend live events to find a specific community of targets, making it more expensive and time-consuming. This has changed now because of social media by changing the way businesses communicate with their audience, allowing start-ups to get in front of a targeted group of people virtually, to make it easier for companies to offer value before asking for anything from a prospect, substituting cards by appearing in the stream of your viewer’s feed, etc. Various business tools like Marketing analytics, networking, product promotions/discounts, informal employee learning/organizational growth, partnership building/loyalty services, and e-commerce can all benefit from social media platforms like Facebook, Instagram, Twitter, Pinterest. Despite the avalanche of possibilities that social media marketing offers, advertisers and brands will also face uphill obstacles in terms of characteristics, evolving customer desires, and other social media patterns and challenges, many brands and social media advertisers fail to stay on top. E-commerce sites employ recommendation algorithms to propose commodities for their clientele. The commodities might well be recommended based on top sellers on a site, based on customer’s demographics or a study of the customer’s previous buying behavior as a forecast for future purchasing behavior. This system remains inactive until the customer finds out about the brand/site and visits the site. With the increase in the businesses interact with customers on social media platforms for various activities we see an opportunity to recognize the customer’s lifestyle, understanding their likes and dislikes in terms of clothing and appetite from the customer’s posts, tags and captions used. Using which E-Commerce platforms can recommend the products fitting the customer’s lifestyle without even waiting for the customer to visit the site.

Published in:

International Journal of Research in Engineering and Science (IJRES- August 2021 Volume 9-Issue 8).


Ramamani Venkatakrishna

Ravi Shukla

Sneha P. Tiwari

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