A tensor recommendation method based on HMM network and meta-path

The main approach to capturing user latent preferences in recommendation systems (RS) is through high-order tensor decomposition and the deep-walk method. Several key issues, if solved, could improve the performance of RS. These include enforcing the interpretation of RS in the context of sparse data completion, cold start, and interpretability, mining user latent preferences with a tensor constructed from a user-item rating matrix (RM) and a preference match mechanism based on K-nearest neighbor (KNN) similar users. In this paper, a method that integrates a hidden Markov model, meta-path, and third-order tensor (HMM-MP-TOT) is proposed. An HMM, based on the user-item RM and latent preferences from KNN users is constructed. Subsequently, the Viterbi and deep-walk methods are used to obtain a series of user-item two-dimensional MPs. Then, truncated − singular value decomposition (t-SVD) is applied to a user-item-KNN third-order tensor to obtain a better recommendation result. On average, HMM-MP-TOT obtains 94.7% precision, 80.2% recall, and 96.4% diversity.