Deep latent factor model for collaborative filtering
مدل فاکتور نهفته عمیق برای فیلتر مشارکتی-2019
Latent factor models have been used widely in collaborative filtering based recommender systems. In re- cent years, deep learning has been successful in solving a wide variety of machine learning problems. Mo- tivated by the success of deep learning, we propose a deeper version of latent factor model. Experiments on benchmark datasets shows that our proposed technique significantly outperforms all state-of-the-art collaborative filtering techniques.
Keywords: Deep learning | Latent semantic analysis | Collaborative filtering | Recommender systems
Could LSA become a “Bifactor”model? Towards a model with general and group factors
آیا LSA می تواند به یک مدل Bifactor تبدیل شود؟ به سمت یک مدل با عوامل کلی و گروهی-2019
One insufficiently grounded criticism made against Latent Semantic Analysis is that it is impossible to semantically interpret its dimensions. This is not true, as several studies have transformed the latent se- mantic space to interpret them, by means of some methods. One of them is the Inbuilt-Rubric method. Rather than grouping concepts around dimensions, as in Exploratory Factor Analysis based rotation meth- ods, the Inbuilt-Rubric is a method that perform an “a priori”imposition of concepts onto the latent se- mantic space. It uses a confirmatory strategy. This study seeks to propose solutions for two limitations found in the current Inbuilt-Rubric methodology: one solution is inspired by Bifactor Models and the management of common variance of the concepts involved; and the other one is based in randomizing the sequence to perform the process. Both methods outperform the current Inbuilt-Rubric version in rel- evant content detection. The reported improvements can be incorporated into expert systems that use Latent Semantic Analysis and Inbuilt-Rubric in relevant content detection or text classification tasks.
Keywords: Latent semantic analysis | Bifactor model | Distributional semantics | Inbuilt-Rubric method | Rotation | Text assessment