Session-based recommendation with temporal convolutional network to balance numerical gaps

Abstract

A session-based recommendation system recommends the next possible item for users by learning the click session sequences of anonymous users. Considering the session data used is anonymous and few background information is available, it is very challenging to solve these problems. Recently, although the session-based recommendation systems based on neural network have achieved gratifying results, there are still two problems in the existing methods: (1) The value of each dimension in the embedded layer result is a non-zero mean distribution and the numerical gaps are very large. Such numerical gaps will increase the variance of the gradient, hindering the parameter optimization, and thus leading to the final prediction results inaccurate; (2) The previous models cannot effectively learn the long-term dependency information and capture the dependencies between non-adjacent items in the session sequence. To solve the above problems, we propose a Session-based Recommendation with Temporal Convolutional Network to Balance Numerical Gaps model. Specifically, we first normalize the embedded layer results, then constrain the embedded results in the unit hypersphere to reduce their impact on gradient calculation, and finally use Temporal Convolution Network (TCN) complements the multi-layer self-attention network to learn the session sequence.The TCN can obtain large enough receptive fields to fully learn session representation and the short-range item dependence that is missing due to the distraction of attention distribution by the self-attention mechanism, and the self-attention method capture the one-to-one interaction of each item, and obtain the long-term dependence of the item. We have conducted a large number of experiments on three real-world datasets. The results show that, in most cases, our proposed method outperforms the state-of-the-arts methods.