K-plet Recurrent Neural Networks for Sequential Recommendation

Abstract

Recurrent Neural Networks have been successful in learning meaningful representations from sequence data, such as text and speech. However, recurrent neural networks attempt to model only the overall structure of each sequence independently, which is unsuitable for recommendations. In recommendation system, an optimal model should not only capture the global structure, but also the localized relationships. This poses a great challenge in the application of recurrent neural networks to the sequence prediction problem. To tackle this challenge, we incorporate the neighbor sequences into recurrent neural networks to help detect local relationships. Thus we propose a K -plet R ecurrent Neural Network (Kr Network for short) to accommodate multiple sequences jointly, and then introduce two ways to model their interactions between sequences. Experimental results on benchmark datasets show that our proposed architecture Kr Network outperforms state-of-the-art baseline methods in terms of generalization, short-term and long term prediction accuracy.