DGEM: A New Dual-modal Graph Embedding Method in Recommendation System
- URL: http://arxiv.org/abs/2108.04031v1
- Date: Mon, 9 Aug 2021 13:31:56 GMT
- Title: DGEM: A New Dual-modal Graph Embedding Method in Recommendation System
- Authors: Huimin Zhou and Qing Li and Yong Jiang and Rongwei Yang and Zhuyun Qi
- Abstract summary: 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.
We propose the Dual-modal Graph Embedding Method (DGEM) to solve these problems.
- Score: 18.33515434926957
- License: http://creativecommons.org/licenses/by/4.0/
- 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.
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