7,388 research outputs found
Sequential Recommendation with Self-Attentive Multi-Adversarial Network
Recently, deep learning has made significant progress in the task of
sequential recommendation. Existing neural sequential recommenders typically
adopt a generative way trained with Maximum Likelihood Estimation (MLE). When
context information (called factor) is involved, it is difficult to analyze
when and how each individual factor would affect the final recommendation
performance. For this purpose, we take a new perspective and introduce
adversarial learning to sequential recommendation. In this paper, we present a
Multi-Factor Generative Adversarial Network (MFGAN) for explicitly modeling the
effect of context information on sequential recommendation. Specifically, our
proposed MFGAN has two kinds of modules: a Transformer-based generator taking
user behavior sequences as input to recommend the possible next items, and
multiple factor-specific discriminators to evaluate the generated sub-sequence
from the perspectives of different factors. To learn the parameters, we adopt
the classic policy gradient method, and utilize the reward signal of
discriminators for guiding the learning of the generator. Our framework is
flexible to incorporate multiple kinds of factor information, and is able to
trace how each factor contributes to the recommendation decision over time.
Extensive experiments conducted on three real-world datasets demonstrate the
superiority of our proposed model over the state-of-the-art methods, in terms
of effectiveness and interpretability
SALSA-TEXT : self attentive latent space based adversarial text generation
Inspired by the success of self attention mechanism and Transformer
architecture in sequence transduction and image generation applications, we
propose novel self attention-based architectures to improve the performance of
adversarial latent code- based schemes in text generation. Adversarial latent
code-based text generation has recently gained a lot of attention due to their
promising results. In this paper, we take a step to fortify the architectures
used in these setups, specifically AAE and ARAE. We benchmark two latent
code-based methods (AAE and ARAE) designed based on adversarial setups. In our
experiments, the Google sentence compression dataset is utilized to compare our
method with these methods using various objective and subjective measures. The
experiments demonstrate the proposed (self) attention-based models outperform
the state-of-the-art in adversarial code-based text generation.Comment: 10 pages, 3 figures, under review at ICLR 201
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