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 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. ACM Reference Format:

Deep Learning-Based Sequential Recommender Systems: Concepts, Algorithms, and Evaluations

“…In the next sections, we will further summarize and evaluate the factors that might impact the performance of a DL-based model, which is expected to better guide future research. [41], [42] TB RNN [8], [43], [44], [51]- [56] [13], [45]- [50], [57], [58] CNN [24], [25], [60], [61] MLP [69], [71] att.…”

A Spatiotemporal Dilated Convolutional Generative Network for Point-Of-Interest Recommendation

“…However, the above RNNs-based methods depend on a hidden state of the entire past that cannot effectively utilize parallel computing within a check-in sequence. This also results in a speed limit on the model's training and evaluation process [22].…”

“…It abandoned RNN structures and demonstrated that this CNN-based recommender can achieve superior performance to the popular RNN model in the Top-N sequential recommendation task. Yuan et al [22] proposed a simple, efficient, and highly effective convolutional generative network for next-item recommendation, which was capable of learning high-level representation from both short-and long-range item dependencies. However, the above two sequence recommendation methods do not consider the spatiotemporal contextual information, and they are not specialized solutions to POI recommendations.…”

“…Moreover, some studies consider integrating spatiotemporal contextual information into RNN structure to enhance the performance of POI recommendation [20,21]. While RNNs and its variants have shown an impressive capability in modeling check-in sequences, these RNN-based methods depend on a hidden state of the entire past that cannot effectively utilize parallel computation within a check-in sequence and fully learn high-level interactions between features [22]. Consequently, these issues inevitably affect RNNs to further improve their performance when applying to POI recommendation.…”

Towards Neural Mixture Recommender for Long Range Dependent User Sequences

“…For instance, we expect this encoder to capture the purchasing pattern iPhone → iPhone case → iPhone charger if it appears frequently in user sequences. As a result, we believe the Recurrent Neural Network (RNN [36]) and the Temporal Convolutional Network ( [3,53,59]) are fitting potential architectural choices. Such neural architectures have shown superb performances when modeling high-order causalities.…”