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

arXiv: https://arxiv.org/abs/2205.11229 · HTML: https://mlsanity.com/paper/2205.11229/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 20 references that are in the map.
- First posted on arXiv 3 May 2022.

## Builds on (most cited first, 2 of 2)

- [Deep Learning Recommendation Model for Personalization and Recommendation Systems](https://mlsanity.com/paper/1906.00091/) (2019)
- [A Model of the Optimal Selection of Crypto Assets](https://mlsanity.com/paper/1906.09632/) (2019)

## Built on by (most cited first, 2 of 2)

- [NFTs to MARS: Multi-Attention Recommender System for NFTs](https://mlsanity.com/paper/2306.10053/) (2023)
- [A Recommender System for NFT Collectibles with Item Feature](https://mlsanity.com/paper/2403.18305/) (2024)

## Abstract (arXiv)

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.