Oracle Recognition of Oracle Network Based on Ant Colony Algorithm - Shi, Xianjin; Shen, Xiajiong (Frontiers in Physics, 2021)
Recent studies have shown that compared with traditional social networks, networks in which users socialize through interest recommendation have obvious homogeneity characteristics. Recommending topics of interest to users has become one of the main objectives of recommendation systems in such social networks, and the widespread data sparsity in such social networks has become the main problem faced by such recommendation systems. Particularly, in the oracle interest network, this problem is more difficult to solve because there are very few people who read and understand the Oracle. To address this problem, we propose an ant colony algorithm based recognition algorithm that can greatly expand the data in the oracle interest network and thus improve the efficiency of oracle interest network recommendation in this paper. Using the one-to-one correspondence between characters and translation in Oracle rubbings, the Oracle recognition problem is transformed into character matching problem, which can skip manual feature engineering experts, so as to realize efficient Oracle 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 rubbings is assigned according to the coordinates. 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 corresponding to the oracle rubbings. 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 approach 100% in some special oracle rubbings.