papers

Collection of papers I've come across and their summary (if I'm not lazy that day)

Folders containing read papers have "_" at the start of their name, so they are at the top of the list in the folder view.

To read

Title (brief)	Link	Summary
True to the Model or True to the Data?	https://arxiv.org/pdf/2006.16234.pdf	Link
A Parameter-Free Classification Method with Compressors	https://aclanthology.org/2023.findings-acl.426.pdf	Link
Leveraging Ensembles of Recommenders to Compete with Budget Constrained Resources	https://dl.acm.org/doi/pdf/10.1145/3556702.3556845	Link
A Diverse Models Ensemble for Fashion Session-Based Recommendation	https://dl.acm.org/doi/pdf/10.1145/3556702.3556821
Balancing Act: Addressing Popularity Bias in Recommendation Systems	https://dilbertai.wordpress.com/2020/08/09/paper-explained-managing-popularity-bias-in-recommender-systems-with-personalized-re-ranking/	Link
The Rise of Two-Tower Models in Recommender Systems	https://towardsdatascience.com/the-rise-of-two-tower-models-in-recommender-systems-be6217494831	Link
Learning To Rank Diversely	https://medium.com/airbnb-engineering/learning-to-rank-diversely-add6b1929621	Link
Generating your shopping list with AI - recommendations at Picnic	https://blog.picnic.nl/generating-your-shopping-list-with-ai-recommendations-at-picnic-300e716241db	Link
Notifications why less is more (Long running A/B tests)	https://medium.com/@AnalyticsAtMeta/notifications-why-less-is-more-how-facebook-has-been-increasing-both-user-satisfaction-and-app-9463f7325e7d	Link

Name		Name	Last commit message	Last commit date
Latest commit History 26 Commits
A Diverse Models Ensemble for Fashion Session-Based Recommendation		A Diverse Models Ensemble for Fashion Session-Based Recommendation
LLM Quantization		LLM Quantization
Notifications why less is more		Notifications why less is more
Understanding Transformers		Understanding Transformers
_Balancing Act Addressing Popularity Bias in Recommendation Systems		_Balancing Act Addressing Popularity Bias in Recommendation Systems
_Generating your shopping list with AI - recommendations at Picnic		_Generating your shopping list with AI - recommendations at Picnic
_Learning To Rank Diversely		_Learning To Rank Diversely
_Leveraging Ensembles of Recommenders to Compete with Budget Constrained Resources		_Leveraging Ensembles of Recommenders to Compete with Budget Constrained Resources
_NEFTUNE - Noisy embeddings improve instruction finetuning		_NEFTUNE - Noisy embeddings improve instruction finetuning
_The Rise of Two-Tower Models in Recommender Systems		_The Rise of Two-Tower Models in Recommender Systems
_True to the Model or True to the Data		_True to the Model or True to the Data
_“Low-Resource” Text Classification- A Parameter-Free Classification Method with Compressors		_“Low-Resource” Text Classification- A Parameter-Free Classification Method with Compressors
.gitignore		.gitignore
README.md		README.md