152,642 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
Streaming Robust Submodular Maximization: A Partitioned Thresholding Approach
We study the classical problem of maximizing a monotone submodular function
subject to a cardinality constraint k, with two additional twists: (i) elements
arrive in a streaming fashion, and (ii) m items from the algorithm's memory are
removed after the stream is finished. We develop a robust submodular algorithm
STAR-T. It is based on a novel partitioning structure and an exponentially
decreasing thresholding rule. STAR-T makes one pass over the data and retains a
short but robust summary. We show that after the removal of any m elements from
the obtained summary, a simple greedy algorithm STAR-T-GREEDY that runs on the
remaining elements achieves a constant-factor approximation guarantee. In two
different data summarization tasks, we demonstrate that it matches or
outperforms existing greedy and streaming methods, even if they are allowed the
benefit of knowing the removed subset in advance.Comment: To appear in NIPS 201
- …