CALRec: Contrastive Alignment of Generative LLMs For Sequential Recommendation

no code implementations • 3 May 2024 • Yaoyiran Li, Xiang Zhai, Moustafa Alzantot, Keyi Yu, Ivan Vulić, Anna Korhonen, Mohamed Hammad

Building upon the success of Large Language Models (LLMs) in a variety of tasks, researchers have recently explored using LLMs that are pretrained on vast corpora of text for sequential recommendation.

Language Modelling Sequential Recommendation

Detection of Myocardial Infarction Based on Novel Deep Transfer Learning Methods for Urban Healthcare in Smart Cities

no code implementations • 22 Jun 2019 • Ahmed Alghamdi, Mohamed Hammad, Hassan Ugail, Asmaa Abdel-Raheem, Khan Muhammad, Hany S. Khalifa, Ahmed A. Abd El-Latif

In case of using VGG-MI1, we achieved an accuracy, sensitivity, and specificity of 99. 02%, 98. 76%, and 99. 17%, respectively and we achieved an accuracy of 99. 22%, a sensitivity of 99. 15%, and a specificity of 99. 49% with VGG-MI2 model.

Data Augmentation Specificity +1