So sánh phương pháp

	LSTM	Mô hình Rasch
Lĩnh vực	Học sâu	Trắc lượng tâm lý
Họ	Machine learning	Latent structure
Năm ra đời	1997	1960
Người khởi xướng	Hochreiter, S. & Schmidhuber, J.	Georg Rasch
Loại	Recurrent neural network (gated memory cell)	Item Response Theory / Latent trait model
Công trình gốc	Hochreiter, S. & Schmidhuber, J. (1997). Long Short-Term Memory. Neural Computation, 9(8), 1735–1780.	Rasch, G. (1960). Probabilistic Models for Some Intelligence and Attainment Tests. Danish Institute for Educational Research, Copenhagen.
Tên gọi khác	LSTM (Uzun Kısa Dönem Bellek Ağı), long short-term memory, LSTM network, recurrent neural network with memory cells	1PL IRT, one-parameter logistic model, Rasch Modeli — 1PL IRT, 1PL model
Liên quan	5	6
Tóm tắt	LSTM (Long Short-Term Memory) is a recurrent neural network architecture, introduced by Sepp Hochreiter and Jürgen Schmidhuber in 1997, that can learn long-term dependencies in sequential data and is widely used for time-series and sequence prediction. It keeps an internal memory that lets information persist across many time steps.	The Rasch model, introduced by Georg Rasch in 1960, is the simplest member of the Item Response Theory (IRT) family. It assigns a single difficulty parameter to each test item and places both item difficulties and person abilities on the same logit scale, enabling direct, sample-independent comparison of items and persons.