Abstract: To solve the issue of low recommendation performance caused by massive high-dimensional data in the blockchain environment, the local sensitive hash algorithm is optimized to reduce the calculation and storage overhead in the nearest neighbor search process. The principal component of the data distribution is used to reduce the poorly captured projection direction in the traditional LSH. Meanwhile, the projection vector weight is quantified, the interval of the hash bucket is adjusted, and the query result set is further refined according to the number of conflicts. Finally, a weighted average strategy is used to predict the score and generate a recommendation list. Experiments show that, compared with other algorithm indexes, the optimized LSH only needs a small amount of hash tables and hash functions to obtain accurate neighbor search results, and the search efficiency is greatly improved. The optimized LSH can well adapt to the characteristics of blockchain data, alleviate the impact of high-dimensional large-scale data on recommendation performance, and improve the recommendation quality and efficiency.

Key words: locality sensitive hashing, blockchain, data distribution, recommendation performance, nearest neighbor search