Hybridization of PMF and LSTM for Recommendation of Intelligent Resource

Nowadays, people live in an era of information explosion. People have to spend a lot of time and energy to filter their own resources every day. How to intelligently recommend resources to users is a big challenge. At the same time, the sparseness of user-to-item rating data is one of the major factors that deteriorate the quality of the recommendation system. In order to deal with the problem, several recommended techniques have been proposed, which additionally consider the auxiliary information to improve the accuracy of the rating prediction, such as textual data for comments, summaries, or summaries. However, due to the inherent limitations of the traditional word pocket model, the contextual information of the document will be difficult to be effectively utilized. Therefore, this paper presents a novel context-aware recommendation Long Short Term matrix decomposition (LstmMF) that integrates the Long Short Term neural network (LSTM) into the probability matrix factor (PMF) to capture the context information of the review document and further improve the accuracy of the rating prediction. The extensive evaluation of the movielens dataset shows that LstmMF is significantly superior to the most advanced recommendation model and has a 83.6% accuracy even if the rating data is very sparse.