Factorizing time-heterogeneous Markov transition for temporal recommendation

Abstract

Temporal recommendation which recommends items to users with consideration of time information has been of wide interest in recent years. But huge event space, highly sparse user activities and time-heterogeneous dependency of temporal behaviors make it really challenging to learn the temporal patterns for high-quality recommendation. In this paper, aiming to handle these challenges, especially the time-heterogeneous characteristic of user’s temporal behaviors, we proposed the Neural-based Time-heterogenous Markov Transition (NeuralTMT) model. Firstly, users’ temporal behaviors are mathematically simplified as the third-order Markov transition tensors. And then a linear co-factorization model which learns the time-evolving user/item factors from these tensors is proposed. Furthermore, the model is extended to the neural-based learning framework (NeuralTMT), which is more flexible and able to capture time-heterogeneous temporal patterns via nonlinear neural network mappings and attention techniques. Extensive experiments on four datasets demonstrate that NeuralTMT performs significantly better than the state-of-the-art baselines. And the proposed method is fundamentally inspired by factorization techniques, which may also provide some interesting ideas on the connection of tensor factorization and neural-based sequential recommendation methods.