Personalized Top-N Sequential Recommendation via Convolutional Sequence Embedding (1809.07426v1)

Abstract: Top-$N$ sequential recommendation models each user as a sequence of items interacted in the past and aims to predict top-$N$ ranked items that a user will likely interact in a near future'. The order of interaction implies that sequential patterns play an important role where more recent items in a sequence have a larger impact on the next item. In this paper, we propose a Convolutional Sequence Embedding Recommendation Model (\emph{Caser}) as a solution to address this requirement. The idea is to embed a sequence of recent items into animage' in the time and latent spaces and learn sequential patterns as local features of the image using convolutional filters. This approach provides a unified and flexible network structure for capturing both general preferences and sequential patterns. The experiments on public datasets demonstrated that Caser consistently outperforms state-of-the-art sequential recommendation methods on a variety of common evaluation metrics.

• The paper introduces Caser, a convolutional sequence embedding model that integrates CNN filters to capture both union-level and point-level sequential patterns.
• It employs horizontal and vertical convolutional layers to model local and global dependencies in user interactions.
• Experiments on four datasets demonstrate that Caser outperforms state-of-the-art models in metrics such as Precision, Recall, and MAP.

Personalized Top-N Sequential Recommendation via Convolutional Sequence Embedding

The paper by Jiaxi Tang and Ke Wang introduces Caser, a Convolutional Sequence Embedding Recommendation Model, as a novel framework for top-$N$ sequential recommendation tasks. It aims to capture both general user preferences and sequential patterns by leveraging the capabilities of Convolutional Neural Networks (CNNs).

Background and Motivation

Sequential recommendation systems are designed to predict items a user will interact with in the near future by modeling the sequence of the user's past interactions. The distinctive feature of sequential recommendation, as opposed to traditional recommendation models, is its emphasis on the order of user actions. This characteristic allows the model to account for both the temporal proximity and sequential patterns in user behavior.

Limitations of Existing Work

Prior models such as those based on Markov Chains (e.g., FPMC, Fossil) have limitations in effectively capturing union-level sequential patterns and skip behaviors. For example, traditional Markov Chain models treat the sequential influence of each action individually rather than collectively, which may not encapsulate the user's inherent sequential behavior. Such models also fail to account for the impact of distant actions that may skip intermediate steps.

Caser: Convolutional Sequence Embedding

Caser introduces a novel approach by transforming a sequence of recent items into an "image" within the time and latent spaces. This image is processed using convolutional filters to detect local sequential patterns. The model incorporates two types of convolutional layers:

1. Horizontal Convolutional Layers: These capture union-level patterns by applying filters horizontally on the 'image.' For instance, these filters can simultaneously recognize patterns like "buying both milk and butter increases the likelihood of buying flour."
2. Vertical Convolutional Layers: These serve to capture point-level patterns by aggregating the weighted sum of previous item embeddings, similar to traditional latent factor models but generalized to high-order sequential dependencies.

Network Architecture

The architecture of Caser integrates three main components:

• Embedding Look-up: This retrieves and stacks the embeddings of the previous $L$ items in a sequence.
• Convolutional Layers: These comprise both horizontal and vertical filters to capture union-level and point-level sequential patterns, respectively.
• Fully-connected Layers: These layers aggregate the outputs of the convolutional layers with user embeddings to incorporate general user preferences.

Experiments and Results

The experimental evaluation was conducted on four datasets—MovieLens, Gowalla, Foursquare, and Tmall—selected based on their sequential intensity derived through sequential association rule mining.

Caser consistently outperformed state-of-the-art models such as FPMC, Fossil, and GRU4Rec across various metrics, including Precision@$N$, Recall@$N$, and Mean Average Precision (MAP). This superior performance underscores the efficacy of capturing both union-level influences and skip behaviors.

Contributions and Future Directions

The contributions of the paper are manifold:

• Unified Framework: Caser integrates convolutional filters within a neural network to capture comprehensive sequential patterns along with general user preferences.
• Enhanced Sequential Modeling: The dual-layered convolutional approach effectively captures both union-level and point-level sequential dependencies.
• Empirical Validation: Extensive experiments confirm Caser's robustness and efficiency over traditional models in various real-life applications.

Future developments could involve the exploration of more complex convolutional architectures or hybrid models combining RNNs with convolutional layers to further enhance sequential dependency capture. Additionally, extending Caser to handle cold-start problems or integrating richer contextual information could provide further improvements.

Conclusion

Caser represents a significant advance in sequential recommendation systems by utilizing convolutional sequence embedding to integrate diverse sequential patterns and user preferences into a single holistic framework. This method demonstrates substantial improvements in recommendation accuracy and provides a foundational basis for future research in sequential recommendations.

Overall, the paper contributes to the field by addressing key limitations of existing models and presenting a powerful approach to harness the sequential nature of user interactions for more accurate top-$N$ recommendations.