# An Analysis of the Features Considerable for NFT Recommendations

Dinuka Piyadigama  Computer Science and Engineering
University of Westminster
London, UK
<EMAIL_ADDRESS>Guhanathan Poravi  Department of Computing
Informatics Institute of Technology
Colombo 06, Sri Lanka
<EMAIL_ADDRESS>
###### Abstract

This research explores the methods that Non-fungible Token (NFT) s can be
recommended to people who interact with NFT-marketplaces to explore NFT s of
preference and similarity to what they have been searching for. While
exploring past methods that can be adopted for recommendations, the use of NFT
traits for recommendations has been explored. The outcome of the research
highlights the necessity of using multiple Recommender Systems to present the
user with the best possible NFT s when interacting with decentralized systems.

###### Index Terms:

Non-fungible Tokens, Recommender Systems, Information Retrieval, Data mining,
Data science

##  I Introduction

In recent months, the NFT market has been growing exponentially as it appears
to be the most widely accepted business application of Blockchain technology
[1], since the introduction of crypto. With more and more people expected to
enter connected digital environments such as the meta-verse [2], it is clear
that NFT s will play a huge role in tomorrow’s internet [3] due to its ability
to make digital items have scarcity, uniqueness, and proof of ownership,
similar to physical items [4]. Human interactions of the next decade on the
internet may entirely rely on NFT s.

###  I-A What are NFT s?

NFT s are provably scarce unique digital assets that can be used to represent
ownership [5]. They can be one-of-a-kind rare artworks, collectible trading
cards, and other assets with the potential to increase in value due to
scarcity [6, 7]. While being digital assets, they also can be used to
represent physical assets. A digital certificate of land/ qualification can be
identified as a couple of examples. The biggest winners in the NFT space over
the last few months have been digital artists who were able to sell art worth
over $2.5 Billion [8].

NFTs were introduced by Ethereum [9] as an improvement proposal [10, 11] in
the Ethereum Request for Comments (ERC)-721 standard [5]. This allows anyone
to implement a Smart Contract with the ERC-721 standard and let people mint
NFTs as well as, keep track of the tokens produced by it. This allows the
created tokens to be validated.

###  I-B Smart Contracts & ERC standards

Smart Contracts are code that is running on the Blockchain. 3 of the notable
ERC standards can be identified in table I.

TABLE I: Comparison of ERC standards ERC-721 |  ERC-777 |  ERC-1155 |  ERC-20
---|---|---|---
Non-fungible tokens |  Non-fungible tokens [12] |  Semi-fungible tokens [13] |  Fungible tokens
Each token is completely unique |  A richer standard for fungible tokens, enabling new use cases and building on past learnings. Backwards compatible with ERC20. |  Tokens begin trading as fungible tokens, then may end up being non-fungible in the long run |  All coins of one kind are equivalent and hold the same value
CryptoKitties [14] |  |  Concert tickets, gift vouchers, coupons |  Crypto currencies - Bitcoin, ETH

Each of the created tokens is unique from the other tokens created by the same
Smart Contract, unlike fungible tokens which were introduced with
cryptocurrencies and are denoted by the ERC-20 standard [15] on the Ethereum
network. One Bitcoin can be swapped withn another Bitcoin, but each NFT will
be unique. Then, the deployed Smart Contract will be responsible to keep track
of the tokens created by it on the network. A Smart Contract is a program that
resides on the Ethereum network with a collection of code & data [16].

For each NFT, the contact address & unit256 tokenId are globally unique on any
blockchain. This allows Decentralized Applications (DApps) [17, 18] to take
the tokenId and present the image/ asset that is identified by the particular
NFT.

###  I-C NFT Marketplaces

OpenSea, which was the first NFT marketplace is also considered to be the
largest. In the attempt to become the ”Amazon of NFTs”, OepnSea raised $23
million in a Series A [19], following a $100 million raise in a Series B
round, ended the company in a valuation of $1.5 billion [20, 21]. Open Sea saw
nearly $150 million in sales in the month of June. These marketplaces are set
to increase access to the digital goods industry [22].

An NFT purchased on an Ethereum marketplace can be traded on any other
Ethereum marketplace for a completely different NFT. Creators don’t
necessarily need to sell their NFT on a market. They can do the transaction
peer-to-peer, completely secured by Blockchain. No one is needed to
intermediate and an owner isn’t locked onto any platform [5].

##  II Motivation to explore how to Recommend NFT s

Recommendation System (RecSys) s play a significant role in the resolution of
the problem of information overload [23]. In order to provide ideal
recommendations to a user, it is important to understand the user’s thought
process as well as other factors that affect a decision to trade.

Recommendation Systems have been driving engagement and consumption of content
as well as items on almost every corner of the internet over the last decade.

These systems help users identify relevant items on an online platform. When
users are recommended relevant items, it enables businesses in growing their
revenue. 35% of Amazon’s revenue [24] & 60% of watch time on YouTube [25]
comes from recommendations. 75% of Netlfix viewer activity [26] was also said
to come from recommendations back in 2013.

Therefore, it is clear that the use of a recommendation system that is catered
toward the needs of potential NFT owners will help increase sales of NFT s,
driving forward the adoption of this technology

Since generating relevant recommendations are highly important for many
business use-cases and the NFT domain is seeing a booming acceptance with a
bright future ahead, this work is expected to add value to the progression of
advancements & accessibility related to the domains of NFT s, Blockchain &
Recommendation Systems.

In this research, the author attempts to identify features that could be
considered for recommending NFTs and the importance of using multiple feature
sets and algorithms to recommend relevant items.

##  III Value-driving factors of NFT s

###  III-A Benefits of NFTs for creators, collectors & buyers

NFTs have a feature to allow a creator to make a certain percentage as royalty
whenever the NFT is transferred to a new buyer. Since the items can be
verified on the Blockchain, it also ensures that the original creator of the
NFT can be tracked down and given due credit, on any date in the future, no
matter how many wallets it gets passed through [22]. Apart from the fact that
a buyer can claim the right of ownership of the original item, they also get
to financially support the creator. Ultimately, NFTs may gain value over time
due to their scarcity. This gives collectors an additional advantage of being
able to sell it for a higher price later on.

Creators of NFTs can also create ”shares” for their NFT. This allows investors
and fans to own a portion of an NFT without having to purchase the entire
thing [5].

###  III-B Pricing of NFT s

When considering the ownership desire of NFT s, it is understood that the
increase in the price of an NFT has the possibility of being a factor to be
considered when making a purchase.

The very first study done examining the pricing of NFTs suggests that
_”prospects for future studies are potentially limitless, as at the beginning
of any new market”_ [27]. As a future study, the author has suggested
identifying if there’s a fundamental model that drives the price determination
in NFTs.

> _”The value of an NFT is entirely determined by what someone else is willing
> to pay for it.”_
> [6]

The value of an NFT has been identified to be heavily reliant on the public’s
acceptance of the item. Demand is expected to drive price rather than
technical, or economic indicators which are the usual factors that affect
stock prices and investor demand.

> _”Ultimately owning the real thing is as valuable as the market makes it.
> The more a piece of content is screen-grabbed, shared, and generally used
> the more value it gains. Owning the verifiable real thing will always have
> more value than not.”_
> [5]

In addition to gaining value, due to the ”non-fungible” nature of the item, it
cannot be replicated. Similar to a Mona Lisa painting, popularity helps
improve the value of the original, and only the original is identified as the
truly original painting with immense value, even though anyone can Google and
get a copy of the painting.

It is understood that NFT s have very little spill-over with other Crypto
assets. However, knowing Crypto price prediction models is important since
Wavelet coherence analysis indicates a co-movement between these two markets
[1]. These models can be used separately on each NFT asset to anticipate the
pricing related to time, sales & bids.

##  IV Existing Work

###  IV-A NFT Collections Recommendation System

Conderation of the use of a basic Machine Learning (ML) technique called
Multiple Regression with data gathered from OpenSea in a blog article on
_OpenSea_ [28].

This takes into account previous purchase patterns and NFTs held in wallets to
predict whether another wallet carrying a similar combination is likely to own
an NFT from a certain category in the future. The categories considered here
are mostly collections created by specific well-known creators. Cryptokitties
and ENS domains are a couple of examples of collections that have been taken
into consideration.

As a final recommendation, this system is capable of presenting NFT
categories. Since users can’t purchase an entire category, they will have to
go back to the process of picking which NFT to purchase in the recommended
collection.

This doesn’t take into consideration of current global trends and it will not
take into account the creators’ recognition. An NFT minted by Beeple or a
major league like NBA is bound to capture more attention of buyers compared to
an NFT minted by a person who hasn’t gained any reputation in this space. The
major concern regarding this system is that the user must either enter his
preferences manually or provide his wallet key, which holds all of his owned
assets, to get a recommendation from the system. Although getting a users’
public key can by no means cause any threat of losing the NFT s, it can lead
to a lack of privacy, which is a tradition that the people into crypto-related
assets have a tendency to be concerned about.

###  IV-B Data Mining NFT Data from OpenSea

One recent study done on data mining and visualizing has made use of the
OpenSea Assets & Events APIs using Python & Pandas to collect, visualize &
analyse NFT data on Meebits Collection [29] NFT sales [30].

This work analyzes the outputs of the following data in the dataset.

  1. 1.

Top 10 Meebits Creators, Buyers & Sellers

  2. 2.

The total number of Meebit Creators and Owners

  3. 3.

Stats about Bundle/Single Sales

  4. 4.

Types of Payment Currencies

  5. 5.

Total Number of Sales per Day

  6. 6.

Total Sales per Day in ETH & USD

  7. 7.

Average, Max & Floor Meebit Price per Day in ETH

While this work helps a lot with data mining, cleaning, preprocessing the
data, and identifying the best possible users to target from a business
perspective, it’s doesn’t explore how recommendations can be generated using
the available data for specific items within the dataset.

###  IV-C What may be the reasons for the lack of research related to
recommending NFT s?

> _”Crypto has a founding tradition of emphasizing freedom and privacy. Maybe
> because of this prevailing cultural trend, the NFT space does not have many
> recommender systems.”_
> [28]

As mentioned in the same blog post, this tradition is also been identified as
a reason why we have not yet seen much development related to Recommendation
Systems in this space. Another reason could be the very recent spark in
interest this domain has seen in recent times.

##  V Proposed Approaches for Recommendations

When conducting a requirement survey prior to building the prototype, the
author understood that there was a clear necessity for NFT creators, buyers &
sellers to find items based on traits of an NFT. Traits are the properties
that describe whatever that is contained in the image/ NFT asset.

Recommending items using NFT traits was attempted by the author in 2 different
ways.

The Bored Ape Yacht Club’s 10,000 NFT s were used to generate recommendations
in this research. The Reference Id represented in the graphs is in the format
of NFT Contract Address - Token Id

###  V-A Trait Similarity Content-based Recommendations Approach

The trait type and value were combined as lowercased strings to create a
single string that would be unique even if NFT s from multiple collections
were used to generate the cosine similarity matrix. A Count-Vectorizer was
used to get a vectorized similarity score between all items considered for
recommendations. The reason for choosing a Count-Vectorizer over a Tf-Idf
Vectorizer was because all traits were considered equally important, to
calculate an aggregate similarity score of all traits per item.

The top 10 items that had the cosine similarity score of the reference item’s
traits were taken as the recommendations here.

Figure 1: Trait Similarity Content based recommendations

###  V-B Trait Rarity based Recommendations Approach

The rarer the traits are, the more valuable it would make an NFT. A rarity-
score calculation method was introduced by rarity tools to calculate the total
rarity-score of an item [31].

$T_{r,t}=\sum^{Nt}_{t=1}\frac{1}{\left(\frac{c_{t}}{T_{N}}\right)}$ (1) Figure
2: Equation to calculate the total trait rarity score of an NFT [32]

$T_{r,t}$ \- Total rarity of a trait
$Nt$ \- Total number of traits in the NFT
$c_{t}$ \- Trait count of the chosen trait (number of occurrences in the
collection) $T_{N}$ \- Total supply of NFT s in the collection

The absolute difference between the total rarities is calculated when an NFT
from a collection is chosen. The lowest scoring items are recommended to the
user. This gives the NFT s that may be as closely valuable as the initially
chosen NFT.

The top 10 items that had the total rarity as close as possible to the
reference item’s rarity were taken as the recommendations here.

Figure 3: Total Rarity based recommendations

##  VI Evaluation

The perfect measurement of evaluating a Recommendation System hasn’t been the
most straightforward. Since the Recommendation Systems introduced in this
research attempt to recommend items in a very specific domain, the author
decided to place the outputs produced by the two Recommendation Models in
opposite graphs of measurements to visualize & evaluate the produced outputs.

Figure 4: Cosine similarities of recommendations generated by both models

Figure 5: Total rarities of recommendations generated by both models

In the generated graphs represented in figures 4 & 5, the item used to
generate recommendations for is represented in orange, the ones in blue were
generated by the Trait similarity content based model, and the ones
represented in green were generated by the Trait rarity model. When taking a
look at Figure 4, it’s clear that none of the items recommended by the trait
rarity model were even close to being content-wise similar.

When taking a look at Figure 5, the items recommended by the cosine similarity
model were scattered around inconsistently.

As it has already been established that both these methods are necessary to be
of value to a user when searching for items, it is clear that both the models
are required to generate recommendations for a particular NFT using its
traits.

##  VII Conclusion

In this research, the author set about exploring work related to utilizing
available features of NFT s to recommend NFT s to users. After heading into
the possibilities of recommending NFT s using their traits, the outputs
produced by two suggested Recommendation Models were depicted in graphs.
Finally, the importance of the two models were explained due to the outputs
produced being contrastingly diverse. The foundation laid by the findings of
this research could be built upon in future work to make the explorability and
relevance of recommended items. This could help create better links between
NFT s and users, resulting in better connections & interactions among users as
well as digital assets on the internet in the next decade.

##  VIII Future Enhancements

If required to limit the recommendations produced, in order to recommend a
fewer number of items with the most value to a user, it may be valuable to
take the user’s preference of each output into consideration. This could be
done in a future study involving responses from human subjects.

Although the author initially planned to attempt recommending items using an
Long short-term memory (LSTM) Deep learning (DL) model since similar attempts
have been researched with cryptocurrencies [33], it was understood that the
very nature of uniqueness brought forwards by NFT s would not produce
consistent results across collections or even items within the same
collection. Even to attempt creating a bid price prediction model was
challenging due to the lack of data and strict rate limits in the OpenSea
Application Programming Interface (API). Closer to the completion of this
research, the author came across an open dataset [34] that may be usable for
this purpose.

During the evaluation phase of the project, one of the feedback received
regarding this was to attempt recommending NFT s using a dataset of price
fluctuations in expensive, valuable physical artworks.

Furthermore, sentiment analysis is also proposed as future work to be combined
with the LSTM method. This could be used to identify how public sentiment
causes the value of crypto to adjust, in relation to past price fluctuations.
This could be an interesting area to dive into since human-desire can be a
major factor of consideration of the acceptance of a particular item.

Since the acceptance of NFT s seem to have a very common connection with
social media such as Twitter, Reddit & Discord communities, it may be possible
to recommend items using such data.

## Acknowledgment

The author of this paper acknowledges the guidance & evaluation insights
received for the project from Mr. Sharmilan Somasundaram & Mr. Ragu Sivaraman.

## References

  * [1] Michael Dowling  “Is non-fungible token pricing driven by cryptocurrencies? — Elsevier Enhanced Reader”, 2021  DOI: 10.1016/j.frl.2021.102097
  * [2] Casey Newton  “Mark Zuckerberg is betting Facebook’s future on the metaverse - The Verge”, 2021  URL: https://www.theverge.com/22588022/mark-zuckerberg-facebook-ceo-metaverse-interview
  * [3] Peter Allen Clark  “What is the Metaverse? Here’s Why It Matters — Time”, 2021  URL: https://time.com/6116826/what-is-the-metaverse/
  * [4] “Non-fungible tokens (NFT)”  URL: https://ethereum.org
  * [5] “ERC-721 Non-Fungible Token Standard”  URL: https://ethereum.org
  * [6] Robyn Conti  “What You Need To Know About Non-Fungible Tokens (NFTs)” Section: Investing, 2021  URL: https://www.forbes.com/advisor/investing/nft-non-fungible-token/
  * [7] Joshua Fairfield  “Tokenized: The Law of Non-Fungible Tokens and Unique Digital Property”, 2021  URL: https://papers.ssrn.com/abstract=3821102
  * [8] “Off the chain: NFT market surges to $2.5B so far this year”, 2021  URL: https://www.aljazeera.com/economy/2021/7/5/off-the-chain-nft-market-surges-to-2-5b-so-far-in-2021
  * [9] Dr Gavin Wood  “ETHEREUM: A SECURE DECENTRALISED GENERALISED TRANSACTION LEDGER”, 2014, pp. 39
  * [10] “EIP-2309: ERC-721 Consecutive Transfer Extension”  URL: https://eips.ethereum.org/EIPS/eip-2309
  * [11] “ERC”  URL: https://eips.ethereum.org/erc
  * [12] Jacques Dafflon, Jordi Baylina and Thomas Shababi  “EIP-777: Token Standard”, 2017  URL: https://eips.ethereum.org/EIPS/eip-777
  * [13] Madana Prathap  “Semi-fungible tokens — coins that travel between the worlds of cryptocurrency and NFTs”, 2021  URL: https://www.businessinsider.in/investment/news/what-is-a-semi-fungible-token-sft/articleshow/87205941.cms
  * [14] CryptoKitties  “CryptoKitties — Collect and breed digital cats!”  URL: https://www.cryptokitties.co
  * [15] “ERC-20 Token Standard”  URL: https://ethereum.org
  * [16] “Introduction to smart contracts”  URL: https://ethereum.org
  * [17] Jake Frankenfield  “Decentralized Applications – dApps”  URL: https://www.investopedia.com/terms/d/decentralized-applications-dapps.asp
  * [18] “Decentralized applications (dapps)”, 2021  URL: https://ethereum.org
  * [19] Robert Hackett  “This crypto marketplace just raised $23 million to be the ‘Amazon of NFTs”’, 2021  URL: https://fortune.com/2021/03/18/nft-art-crypto-marketplace-opensea-amazon/
  * [20] dfinzer  “Announcing our $100M raise, led by a16z” Section: Company Announcements, 2021  URL: https://opensea.io/blog/announcements/announcing-our-100m-raise-led-by-a16z/
  * [21] Lucas Matney  “NFT market OpenSea hits $1.5 billion valuation”, 2021  URL: https://social.techcrunch.com/2021/07/20/nft-market-opensea-hits-1-5-billion-valuation/
  * [22] Sylve Chevet  “Blockchain Technology and Non-Fungible Tokens: Reshaping Value Chains in Creative Industries”  In _SSRN Electronic Journal_ , 2018  DOI: 10.2139/ssrn.3212662
  * [23] Li Chen Cheng and Ming-Chan Lin  “A hybrid recommender system for the mining of consumer preferences from their reviews”  In _Journal of Information Science_ 46.5, 2020, pp. 664–682  DOI: 10.1177/0165551519849510
  * [24] Maxim Naumov et al.  “Deep Learning Recommendation Model for Personalization and Recommendation Systems”  In _arXiv:1906.00091 [cs]_ , 2019  arXiv: http://arxiv.org/abs/1906.00091
  * [25] “Recommendations: What and Why? — Recommendation Systems”  URL: https://developers.google.com/machine-learning/recommendation/overview
  * [26] Tom Vanderbilt  “The Science Behind the Netflix Algorithms That Decide What You’ll Watch Next” Section: tags  In _Wired_ URL: https://www.wired.com/2013/08/qq-netflix-algorithm/
  * [27] Michael Dowling  “Fertile LAND: Pricing non-fungible tokens”  In _Finance Research Letters_ , 2021, pp. 102096  DOI: 10.1016/j.frl.2021.102096
  * [28] “What are you missing? Using basic machine learning to predict and recommend NFTs with OpenSea data - OpenSea Blog”, 2020  URL: https://opensea.io/blog/analysis/predict-and-recommend-nfts/
  * [29] Larva Labs  “The Meebits”  URL: https://meebits.larvalabs.com/
  * [30] Adil Moujahid  “Data mining Meebits NFTs using Python and OpenSea API // Adil Moujahid // Data Analytics and more”, 2021  URL: http://adilmoujahid.com/posts/2021/06/data-mining-meebits-nfts-python-opensea/
  * [31] rarity.tools  “Introducing rarity.tools. rarity.tools is a website dedicated to… — by rarity.tools — Medium”, 2021  URL: https://raritytools.medium.com/introducing-rarity-tools-9b0138e992b3
  * [32] rarity.tools  “Ranking Rarity: Understanding Rarity Calculation Methods”, 2021  URL: https://raritytools.medium.com/ranking-rarity-understanding-rarity-calculation-methods-86ceaeb9b98c
  * [33] Ferdiansyah Ferdiansyah et al.  “A LSTM-Method for Bitcoin Price Prediction: A Case Study Yahoo Finance Stock Market”  In _2019 International Conference on Electrical Engineering and Computer Science (ICECOS)_ , 2019, pp. 206–210  DOI: 10.1109/ICECOS47637.2019.8984499
  * [34] ZOMGLINGS and Avery Smith  “Ethereum NFTs — Kaggle”, 2021  URL: https://www.kaggle.com/datasets/simiotic/ethereum-nfts