# Non-Negative PARATUCK2 Tensor Decomposition Combined to LSTM Network For Smart Contracts Profiling

## TL;DR

This paper introduces a novel method combining non-negative PARATUCK2 tensor decomposition with LSTM networks to analyze and forecast smart contract interactions over time, validated on smart contract and video recommendation use cases.

## Contribution

The paper presents an innovative approach integrating tensor decomposition with LSTM for temporal analysis of smart contracts, a novel application in this domain.

## Key findings

- Effective forecasting of smart contract interactions.
- Successful application to video on demand recommendation.
- Provides insights into complex smart contract behaviors.

## Abstract

Smart contracts are programs stored and executed on a blockchain. The Ethereum platform, an open-source blockchain-based platform, has been designed to use these programs offering secured protocols and transaction costs reduction. The Ethereum Virtual Machine performs smart contracts runs, where the execution of each contract is limited to the amount of gas required to execute the operations described in the code. Each gas unit must be paid using Ether, the crypto-currency of the platform. Due to smart contracts interactions evolving over time, analyzing the behavior of smart contracts is very challenging. We address this challenge in our paper. We develop for this purpose an innovative approach based on the non-negative tensor decomposition PARATUCK2 combined with long short-term memory (LSTM) to assess if predictive analysis can forecast smart contracts interactions over time. To validate our methodology, we report results for two use cases. The main use case is related to analyzing smart contracts and allows shedding some light into the complex interactions among smart contracts. In order to show the generality of our method on other use cases, we also report its performance on video on demand recommendation.