A case weighted similarity deep measurement method based on a self-attention Siamese neural network

Abstract

To improve the accuracy of similarity measures in case-based reasoning, in this paper, we propose a deep metric learning method based on a self-attention mechanism and a Siamese neural network to realize the weighted similarity measure between cases. In this method, weight assignment and similarity measurement processes are integrated into a deep network. The method can map cases to a new feature space through nonlinear processing of the network layer to obtain better feature representation. The inner relationship between the features is captured by the self-attention mechanism, which is connected to the previous network layer, and the weight of the features is determined by the scoring function. Finally, a metric function is added to the contrastive loss to measure the case similarity. Experiments show that the accuracy of this method is better than that of other algorithms in the similarity measure and can improve the accuracy of case retrieval.

Generating Attribute Similarity Graphs: A User Behavior-Based Approach from Real- Time Microblogging Data on Platform X

“…Feature weights have a substantial impact on similarity calculations. They regulate the impact or signi cance of each attribute in determining the overall similarity between users(Cheng & Yan, 2023;Shantal et al, 2023). For instance, the attribute 'Number of followers' has a weight of 0.5, while "Number of tweets" has a weight of 0.2.…”