Deep Recommendation With Adversarial Training

Chenyan Zhang

doi:10.1109/TETC.2022.3141422

Deep Recommendation With Adversarial Training

2022-10-01

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AI

DOI：10.1109/TETC.2022.3141422

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Recommender systems provide an effective solution to the information overload and have become a research hotspot in both industry and academia. Some existing work on implicit feedback has made great progress. However, there are still the following shortcomings: 1) existing pairwise ranking methods mostly sample unobserved items uniformly to obtain negative samples, which brings a biased solution and insufficient convergence; 2) the recommendation result comes from a complicated process, which makes it less interpretable. Therefore, we put forward a Deep Recommendation with Adversarial Training (DRAT) model, which provides users with personalized recommendations by utilizing an encoder-decoder structure and adversarial training. It consists of two components: one is feature learning module in which the encoder captures the text features of items from user-generated reviews, and the decoder reconstructs the text with the captured features; the other is rating prediction module which utilizes adversarial training to generate personalized negative samples tackling the drawbacks of uniform negative sampling. Extensive experiments on five real-world datasets show that our proposed model significantly outperforms the baseline methods.

Keywords:

Recommender systems

generative adversarial nets

attention mechanism

factorization machine

implicit feedback

Deep Recommendation With Adversarial Training

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