A Simple Convolutional Generative Network for Next Item Recommendation

Abstract

Convolutional Neural Networks (CNNs) have been recently introduced in the domain of session-based next item recommendation. An ordered collection of past items the user has interacted with in a session (or sequence) are embedded into a 2-dimensional latent matrix, and treated as an image. The convolution and pooling operations are then applied to the mapped item embeddings. In this paper, we first examine the typical session-based CNN recommender and show that both the generative model and network architecture are suboptimal when modeling long-range dependencies in the item sequence. To address the issues, we introduce a simple, but very effective generative model that is capable of learning high-level representation from both short- and long-range item dependencies. The network architecture of the proposed model is formed of a stack of \emph{holed} convolutional layers, which can efficiently increase the receptive fields without relying on the pooling operation. Another contribution is the effective use of residual block structure in recommender systems, which can ease the optimization for much deeper networks. The proposed generative model attains state-of-the-art accuracy with less training time in the next item recommendation task. It accordingly can be used as a powerful recommendation baseline to beat in future, especially when there are long sequences of user feedback.

References in corpus (12)

• Adam: A Method for Stochastic Optimization
• WaveNet: A Generative Model for Raw Audio
• Convolutional Sequence to Sequence Learning
• Pixel Recurrent Neural Networks
• Recurrent Neural Networks with Top-k Gains for Session-based Recommendations
• Session-based Recommendations with Recurrent Neural Networks
• Neural Machine Translation in Linear Time
• Improved Recurrent Neural Networks for Session-based Recommendations
• Dense Prediction on Sequences with Time-Dilated Convolutions for Speech Recognition
• A Hierarchical Contextual Attention-based GRU Network for Sequential Recommendation
• Recurrent Latent Variable Networks for Session-Based Recommendation
• Contextual Sequence Modeling for Recommendation with Recurrent Neural Networks

Cited by in corpus (2)

• Hierarchical Temporal Convolutional Networks for Dynamic Recommender Systems
• Future-Aware Diverse Trends Framework for Recommendation