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Deep adversarial autoencoder recommendation algorithm based on group influence
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AI
DOI：10.1016/j.inffus.2023.101903.png)
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Abstract
En 中文
Recommender systems are crucial in the big data era, effectively mitigating information overload. Existing recommendation methods are limited on highly sparse data and have mediocre recall performance. Group influence aggregates knowledge from different users or organizations to generate decisions, improving information fusion efficiency and group decision-making quality. In this paper, a group influence-based deep adversarial autoencoder (GI-AAE), is proposed for top-N recommendation. It leverages group influence to strengthen autoencoder latent features and address sparse data and uses adversarial learning from GANs to enhance reconstruction. The group influence based deep autoencoder (GI-AE) is the generative model for the GI-AAE. Experimental results show that the proposed algorithm is competitive and gets higher recall values.
Keywords:
Recommender system
Deep learning
Autoencoder
Group influence
Group decision-making
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