Modelling item sequences by overlapped Markov embeddings

Logistic Markov Embedding (LME) has become a popular branch on the research of sequential item recommendation. However, since LME is an algorithm with very high time complexity, it has a poor scalability and is not able to carry a huge dataset with many items. Hence, several approaches are designed to decrease the time complexity of LME, while keeping the prediction accuracy. In this paper, we present a new speed-up approach for LME, which convert the original item set into several smaller and overlapped clusters, then train a LME for each cluster. We show that this new clustering algorithm is able to get a better performance in a shorter training time compared to the current best speed-up approach.