A Simple Convolutional Generative Network for Next Item Recommendation (1808.05163v4)

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.

Overview of the Convolutional Generative Network for Next Item Recommendation

The paper presents a convolutional generative model designed to enhance session-based next item recommendation systems. This model addresses several limitations inherent in existing convolutional neural network (CNN) architectures, particularly in handling long-range dependencies within user-item interaction sequences.

Key Contributions and Architecture

The proposed model introduces a stacked architecture of one-dimensional dilated convolutions, eschewing traditional pooling operations. This architectural choice allows an efficient increase in the receptive fields, thereby capturing both short- and long-range dependencies without the spatial information loss typically associated with pooling layers.

Generative Model Design:

The model employs a probabilistic generative approach to model the conditional distributions of user-item interactions. It uniquely factors the joint distribution of sequences to estimate each item's distribution in a sequence. This contrasts with previous models, such as Caser and GRURec, which only estimate the distribution for the next item, often ignoring internal sequential dependencies unless augmented with multiple sub-sessions.

Residual Learning:

The paper incorporates residual networks, enhancing the model's ability to train deeper architectures effectively. This approach alleviates issues like vanishing gradients and improves optimization efficiency, critical for modeling complex item dependencies.

Dilated Convolutions:

Dilated convolutions are leveraged to achieve exponential growth in receptive fields, efficiently modeling long-range sequential patterns. This technique permits broader context analysis without deepening the network significantly, maintaining computational efficiency.

Practical Implications:

The model shows promising results, outperforming state-of-the-art models like GRURec and Caser across various top-N recommendation tasks. It demonstrates superior convergence speeds due to its ability to leverage full sequence information without relying on inefficient training augmentations like sub-session padding.

Evaluation and Results

Empirical evaluations confirm that the proposed model achieves state-of-the-art accuracy in session-based recommendations across several datasets, including datasets with short and long session lengths. Metrics such as MRR@5 and HR@5 highlight substantial improvements over competitive baselines, evidencing the advantage of modeling the complete sequence dependencies.

Training Efficiency:

The model's design allows for full parallelization over sequence elements, markedly reducing training times compared to RNN-based models. This efficiency arises from the inherent parallel capabilities of CNNs and the effective capturing of inter-item dependencies without redundant sub-session models.

Future Directions

The model establishes a robust baseline for future work in sequential recommendation systems. Potential research directions include integrating additional user contexts, such as user identity and location, into the model's predictive capacity. Moreover, exploring adaptive dilated convolution techniques could offer further insights into capturing nuanced sequence dynamics.

In summary, the paper's contributions present a significant advancement in convolutional generative networks for next item recommendation tasks, offering a powerful and efficient alternative to existing RNN and CNN-based models. The model's ability to handle full sequence information positions it as a formidable approach for tackling real-world recommendation challenges.