Improving Diversity with Multi-Loss Adversarial Training in Personalized News Recommendation
Source abstract
Abstract
Users’ interests are often diverse and multi-grained, with their underlying intents even more so. Effectively capturing users’ interests and uncovering the relationships between diverse interests are key to news recommend... | Find, read and cite all the research you need on Tech Science Press
Source: openalex_inverted_index · Quality: baseline
Research context
OpenAlex topics: Computational and Text Analysis Methods · Text and Document Classification Technologies · Sentiment Analysis and Opinion Mining
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
References (35)
Show source-provided references
1. MIND: A largescale dataset for news recommendation
2. Personalized news recommendation based on the text and image integration
3. News recommender system: A review of recent progress, challenges, and opportunities10.1007/s10462-021-10043-x ↗
4. NPA: Neural news recommendation with personalized attention
5. C. Wu, F. Wu, T. Qi, and Y. Huang, “End-to-end learnable diversity-aware news recommendation,” arXiv:2204.00539, 2022. doi: 10.48550/arXiv.2204.00539.
6. Embedding-based news recommendation for millions of users
7. Neural news recommendation with attentive multi-view learning
8. Neural news recommendation with long-and short-term user representations
9. UNBERT: User-news matching BERT for news recommendation
10. Personalized news recommendation: Methods and challenges
11. HieRec: Hierarchical user interest modeling for personalized news recommendation
12. Reducing cross-topic political homogenization in content-based news recommendation
13. Fine-grained interest matching for neural news recommendation
14. DKN: Deep knowledge-aware network for news recommendation
15. Diversification in session-based news recommender systems10.1007/s00779-021-01606-4 ↗
16. PP-Rec: News recommendation with personalized user interest and time-aware news popularity
17. A deep joint network for sessionbased news recommendations with contextual augmentation
18. EDA: Easy data augmentation techniques for boosting performance on text classification tasks
19. Mimicking human verification behavior for news media credibility evaluation10.3390/app13179553 ↗
20. GloVe: Global vectors for word representation
21. Deep sparse rectifier neural networks
22. Fairness-aware news recommendation with decomposed adversarial learning
23. R-AT: Regularized adversarial training for natural language understanding
24. C. Zhu, Y. Cheng, Z. Gan, S. Sun, T. Goldstein and J. Liu, “FreeLB: Enhanced adversarial training for natural language understanding,” 2019. doi: 10.48550/arXiv.1909.11764.
25. D. P. Kingma and J. Ba, “Adam: A method for stochastic optimization,” 2014. doi: 10.48550/arXiv.1412.6980.
26. ProFairRec: Provider fairness-aware news recommendation
27. Neural news recommendation with topic-aware news representation
28. Neural news recommendation with multi-head self-attention
29. News recommendation via multi-interest news sequence modelling
30. Wu · Adv. Neural Inf. Process. Syst. · 2021 · 34 · 10890
31. I. Goodfellow, J. Shlens, and C. Szegedy, “Explaining and harnessing adversarial examples,” 2014. doi: 10.48550/arXiv.1412.6572.
32. T. Miyato, A. Dai, and I. Goodfellow, “Adversarial training methods for semi-supervised text classification,” 2016. doi: 10.48550/arXiv.1605.07725.
33. A. Madry, A. Makelov, L. Schmidt, D. Tsipras, and A. Vladu, “Towards deep learning models resistant to adversarial attacks,” 2017. doi: 10.48550/arXiv.1706.06083.
34. Adversarial training for free!
35. SMART: Robust and efficient fine-tuning for pre-trained natural language models through principled regularized optimization