The traditional heterogeneous embedding method based on a random walk strategy does not focus on the random walk fundamentally because of higher-order Markov chains. One of the important properties of Markov chains is stationary distributions (SDs). However, in large-scale network computation, SDs are not feasible and consume a lot of memory. So, we use a non-Markovian space strategy, i.e., a heterogeneous personalized spacey random walk strategy, to efficiently get SDs between nodes and skip some unimportant intermediate nodes, which allows for more accurate vector representation and memory savings. This heterogeneous personalized spacey random walk strategy was extended to heterogeneous space embedding methods in combination with vector learning, which is better than the traditional heterogeneous embedding methods for node classification tasks. As an excellent embedding method can obtain more accurate vector representations, it is important for the improvement of the recommendation model. In this article, recommendation algorithm research was carried out based on the heterogeneous personalized spacey embedding method. For the problem that the standard random walk strategy used to compute the stationary distribution consumes a large amount of memory, which may lead to inefficient node vector representation, we propose a meta-path-based heterogenous personalized spacey random walk for recommendation (MPHSRec). The meta-path-based heterogeneous personalized spacey random walk strategy is used to generate a meaningful sequence of nodes for network representation learning, and the learned embedded vectors of different meta-paths are transformed by a nonlinear fusion function and integrated into a matrix decomposition model for rating prediction. The experimental results demonstrate that MPHSRec not only improves the accuracy, but also reduces the memory cost compared with other excellent algorithms.
Split-and-Combine Singular Value Decomposition for Large-Scale Matrix