DGEM: A New Dual-modal Graph Embedding Method in Recommendation System

TL;DR

DGEM introduces a dual-modal graph embedding approach that captures high-order item relationships and temporal dynamics, improving recommendation accuracy and efficiency in complex graph-based systems.

Contribution

The paper proposes DGEM, a novel dual-modal graph embedding method that effectively models static and dynamic item relationships for recommendation systems.

Findings

DGEM enhances recommendation accuracy by capturing high-order item proximities.

DGEM improves model convergence speed compared to traditional embedding methods.

DGEM leverages temporal relationships to boost recommendation performance.

Abstract

In the current deep learning based recommendation system, the embedding method is generally employed to complete the conversion from the high-dimensional sparse feature vector to the low-dimensional dense feature vector. However, as the dimension of the input vector of the embedding layer is too large, the addition of the embedding layer significantly slows down the convergence speed of the entire neural network, which is not acceptable in real-world scenarios. In addition, as the interaction between users and items increases and the relationship between items becomes more complicated, the embedding method proposed for sequence data is no longer suitable for graphic data in the current real environment. Therefore, in this paper, we propose the Dual-modal Graph Embedding Method (DGEM) to solve these problems. DGEM includes two modes, static and dynamic. We first construct the item graph to extract the graph structure and use random walk of unequal probability to capture the high-order proximity between the items. Then we generate the graph embedding vector through the Skip-Gram model, and finally feed the downstream deep neural network for the recommendation task. The experimental results show that DGEM can mine the high-order proximity between items and enhance the expression ability of the recommendation model. Meanwhile it also improves the recommendation performance by utilizing the time dependent relationship between items.