Titlebook: Web Information Systems Engineering – WISE 2019; 20th International C Reynold Cheng,Nikos Mamoulis,Xin Huang Conference proceedings 2019 Sp

顯而易見

不規(guī)則的跳動

Generating Adversarial Examples by Adversarial Networks for Semi-supervised Learning a classifier that tries to classify the original samples and the adversarial examples consistently. We evaluate our model on several datasets, and the experimental results show that our model outperforms the state-of-the-art methods for semi-supervised learning. The experiments also demonstrate tha

規(guī)章

Dual Path Convolutional Neural Network for Student Performance Prediction not trivial to construct a good predictive model for some majors with limited student samples. To address the above issues, we develop a novel end-to-end deep learning method and propose Dual Path Convolutional Neural Network (DPCNN) for student performance prediction. Moreover, we introduce multi-

NIP

JEER

Personalized Book Recommendation Based on a Deep Learning Model and Metadatahe book recommendation problem using a deep learning model and various metadata that can infer the content and the quality of books without utilizing the actual content. Metadata, which include Library Congress Subject Heading (LCSH), book description, user ratings and reviews, which are widely avai

obligation

丑惡

中國紀(jì)念碑

Co-purchaser Recommendation Based on Network Embeddinguncated bias walk. Our experimental results on real datasets show that the proposed methods, particularly the latter, can effectively complete the co-purchaser recommendation and has a high recommendation performance.

Minutes

箴言

Memory-Augmented Attention Network for Sequential Recommendationttention network which is stacked on the memory layer. Finally, the mixture of long-term and short-term preference is feeded into the prediction layer to make recommendations. Extensive experiments on four real datasets show that MEANS outperforms various state-of-the-art sequential recommendation m