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

TL;DR

This paper explores integrating social media trend data into NFT recommendation systems to enhance their effectiveness, especially when user-click data is limited or privacy concerns restrict data collection.

Contribution

It introduces a novel scoring mechanism and architecture that combines social trends with recommendation algorithms for NFTs, leveraging decentralized network concepts.

Findings

Improved recommendation relevance using social trend data

Effective integration of multiple information sources

Potential for enhanced user engagement with trend-aware recommendations

Abstract

Recommendations Systems have been identified to be one of the integral elements of driving sales in e-commerce sites. The utilization of opinion mining data extracted from trends has been attempted to improve the recommendations that can be provided by baseline methods in this research when user-click data is lacking or is difficult to be collected due to privacy concerns. Utilizing social trends to influence the recommendations generated for a set of unique items has been explored with the use of a suggested scoring mechanism. Embracing concepts from decentralized networks that are expected to change how users interact via the internet over the next couple of decades, the suggested Recommendations System attempts to make use of multiple sources of information, applying coherent information retrieval techniques to extract probable trending items. The proposed Recommendations Architecture in the research presents a method to integrate social trends with recommendations to produce promising outputs.