A Smart Contract Top-N Recommendation Method Based on Implicit Feedback

Abstract

Smart contracts on Ethereum evolve rapidly and lack explicit feedback, so it becomes necessary to use implicit feedback for recommendations. This paper proposes a collaborative filtering recommendation algorithm based on the user preferences list (UPLS-CF) to solve the above problems. We propose a pseudo rating generator to convert the implicit feedback data into explicit ratings and use collaborative filtering-based recommendation algorithm to complete top-N recommendations for smart contracts. In addition, we introduce user preference information to improve the accuracy of recommendations. Extensive experiments on datasets show that the algorithm proposed in this paper improves Precision by 15.975%, Recall by 12.116%, and HR by 20.815%. The results show that it can improve the algorithm's accuracy and can effectively recommend smart contracts to Ethereum users to combine the proposed algorithm with the user preference information.