A Simple Convolutional Generative Network for Next Item Recommendation

Understand

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.