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Recommendations Systems have been identified to be one of the integralelements of driving sales in e-commerce sites. The utilization of opinionmining data extracted from trends has been attempted to improve therecommendations that can be provided by baseline methods in this research whenuser-click data is lacking or is difficult to be collected due to privacyconcerns.  Utilizing social trends to influence the recommendations generated for a setof unique items has been explored with the use of a suggested scoringmechanism. Embracing concepts from decentralized networks that are expected tochange how users interact via the internet over the next couple of decades, thesuggested Recommendations System attempts to make use of multiple sources ofinformation, applying coherent information retrieval techniques to extractprobable trending items.  The proposed Recommendations Architecture in the research presents a methodto integrate social trends with recommendations to produce promising outputs.

Exploration of the possibility of infusing Social Media Trends into generating NFT Recommendations