Oracle Recognition of Oracle Network Based on Ant Colony Algorithm - Shi, Xianjin; Shen, Xiajiong (Frontiers in Physics, 2021)
Compared with traditional social networks, networks in which users socialize through interest recommendations have obvious homogeneous characteristics, and recommending topics of interest to users has become one of the main goals of recommendation systems in such social networks, while the data sparsity prevailing in such social networks has become the main problem faced by such recommendation systems. To solve this problem, this paper proposes an oracle bones based recognition algorithm that can greatly expand the data in the oracle interest network and thus improve the efficiency of oracle bones interest network recommendation. Using the one-to-one correspondence between characters and transliteration in the oracle bones to turn the oracle recognition problem into a character matching problem, the manual feature engineering by experts can be skipped, thus achieving efficient oracle bones recognition. First, the coordinates of each character in the oracle bones are extracted, then, the matching degree value of each oracle character corresponding to the translation of the oracle bones are assigned according to the coordinates, and finally, the maximum matching degree value of each character is searched using the improved ant colony algorithm, and the search result is the Chinese character character corresponding to the oracle bones. In this paper, through experimental simulation, it is proved that this method is very effective when applied to the field of oracle recognition, and the recognition rate can approach100% in some special oracle bones.