Relationship between stretch zone parameters and fracture toughness of heat-resistance steel

Abstract

Predicting the product a customer would like to buy is an increasingly important field of study and there are several different recommender system models that are used to make recommendations for users. Deep learning has shown effective results in a variety of predictive tasks but there haven’t been much research concerning its usage in recommender systems. This thesis studies the effectiveness of using a long short term memory implementation (LSTM) of a recurrent neural network (RNN) as a recommender system by comparing it to one of the most common recommender system implementations, the matrix factorization method. A radio playlist dataset is used to train both the LSTM and the matrix factorization models with the intent of generating accurate predictions. We were unable to create a LSTM model with good performance and due to that we are unable to make any significant conclusions regarding whether or not LSTM networks outperform matrix factorization models