A Simple Convolutional Generative Network for Next Item Recommendation

  title={A Simple Convolutional Generative Network for Next Item Recommendation},
  author={Fajie Yuan and Alexandros Karatzoglou and Ioannis Arapakis and Joemon M. Jose and Xiangnan He},
  journal={Proceedings of the Twelfth ACM International Conference on Web Search and Data Mining},
Convolutional Neural Networks (CNNs) have been recently introduced in the domain of session-based next item recommendation. [] Key Method The network architecture of the proposed model is formed of a stack of 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…

Figures and Tables from this paper

Multi-Scale Quasi-RNN for Next Item Recommendation

A new neural architecture, multi-scale Quasi-RNN for next item Recommendation (QR-Rec) task, which aims to capture the recurrent relations between different kinds of local features, which has never been studied previously in the context of recommendation.

Self-Supervised Hypergraph Convolutional Networks for Session-based Recommendation

This paper models session-based data as a hypergraph and proposes a dual channel hypergraph convolutional network -- DHCN to improve SBR and innovatively integrates self-supervised learning into the training of the network by maximizing mutual information between the session representations learned via the two channels in D HCN.

A Knowledge-Aware Recommender with Attention-Enhanced Dynamic Convolutional Network

This model combines the dynamic convolutional network with attention mechanisms to capture changing dependencies in the sequence and enhances the representations of items effectively and improve the extractability of sequential dependencies.

Modeling the Past and Future Contexts for Session-based Recommendation

It is argued that users' future action signals can be exploited to boost the recommendation quality and empirical evidence confirms that training sequential recommendation models with future contexts is a promising way to offer better recommendation accuracy.

CosRec: 2D Convolutional Neural Networks for Sequential Recommendation

This paper argues that modeling pairwise relationships directly leads to an efficient representation of sequential features and captures complex item correlations, and proposes a 2D convolutional network for sequential recommendation (CosRec), which outperforms both conventional methods and recent sequence-based approaches.

Pair-Wise Convolution Network with Transformers for Sequential Recommendation

This work proposes a pair-wise convolution network with transformers for the sequential recommendation and adopts a residual connection to prevent the gradient from disappearing and solve the loss of feature information.

A Self-Attention Mask Learning-Based Recommendation System

This paper proposes a sequence recommendation model named GAT4Rec (Gated Recurrent Unit And Transformer For Recommendation), which uses a Transformer layer that shares parameters across layers to model the user's historical interaction sequence.

Integrating the Pre-trained Item Representations with Reformed Self-attention Network for Sequential Recommendation

This paper proposes a self-supervised learning task on item-side information to generate the item representation space in advance of the recommendation task, and reform a recently successful sequential recommendation method, self-attention network, by integrating an untrainable mechanism to fully take advantage of the pre-traineditem representation space.

StackRec: Efficient Training of Very Deep Sequential Recommender Models by Iterative Stacking

This work proposes the stacking operation on the pre-trained layers/blocks to transfer knowledge from a shallower model to a deep model, then performs iterative stacking so as to yield a much deeper but easier-to-train SR model.



Personalized Top-N Sequential Recommendation via Convolutional Sequence Embedding

A Convolutional Sequence Embedding Recommendation Model »Caser» is proposed, which is to embed a sequence of recent items into an image in the time and latent spaces and learn sequential patterns as local features of the image using convolutional filters.

3D Convolutional Networks for Session-based Recommendation with Content Features

A method that combines session clicks and content features such as item descriptions and item categories to generate recommendations and outperformed several baselines and a state-of-the-art method based on recurrent neural networks for add-to-cart events in e-commerce websites.

A Hierarchical Contextual Attention-based GRU Network for Sequential Recommendation

Contextual Sequence Modeling for Recommendation with Recurrent Neural Networks

A new class of Contextual Recurrent Neural Networks for Recommendation (CRNNs) that can take into account the contextual information both in the input and output layers and modifying the behavior of the RNN by combining the context embedding with the item embedding and parametrizing the hidden unit transitions as a function of context information is proposed.

Recurrent Latent Variable Networks for Session-Based Recommendation

This work seeks to devise a machine learning mechanism capable of extracting subtle and complex underlying temporal dynamics in the observed session data, so as to inform the recommendation algorithm, by adopting concepts from the field of Bayesian statistics, namely variational inference.

Neural Attentive Session-based Recommendation

A novel neural networks framework, i.e., Neural Attentive Recommendation Machine (NARM), is proposed to tackle session-based recommendation, which outperforms state-of-the-art baselines on both datasets and achieves a significant improvement on long sessions.

Session-based Recommendations with Recurrent Neural Networks

It is argued that by modeling the whole session, more accurate recommendations can be provided by an RNN-based approach for session-based recommendations, and introduced several modifications to classic RNNs such as a ranking loss function that make it more viable for this specific problem.

Improved Recurrent Neural Networks for Session-based Recommendations

This work proposes the application of two techniques to improve RNN-based models for session-based recommendations performance, namely, data augmentation, and a method to account for shifts in the input data distribution.

Learning Hierarchical Representation Model for NextBasket Recommendation

This paper introduces a novel recommendation approach, namely hierarchical representation model (HRM), which can well capture both sequential behavior and users' general taste by involving transaction and user representations in prediction.

Multi-Scale Context Aggregation by Dilated Convolutions

This work develops a new convolutional network module that is specifically designed for dense prediction, and shows that the presented context module increases the accuracy of state-of-the-art semantic segmentation systems.