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Uncommon Python Tricks in Popular Libraries

2025-07-07

This article unveils lesser-known Python techniques discovered while exploring widely-used libraries. The author highlights using `super()` in base classes for cooperative multiple inheritance, employing mixins for modular feature addition, leveraging relative imports for package-specific searches, and utilizing `__init__.py` beyond package declaration for API simplification and initialization. The article also reveals `conftest.py`'s role in pytest module recognition and the value of studying library design papers for deeper understanding.

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LLMs Revolutionize Recommendation Systems and Search: A Comprehensive Survey

2025-03-23

This article surveys recent research applying Large Language Models (LLMs) to recommendation systems and search engines. Studies explore various approaches, including LLM-augmented model architectures (e.g., YouTube's Semantic IDs and Kuaishou's M3CSR), using LLMs for data generation and analysis (e.g., Bing's Recommendation Quality Improvement and Indeed's Expected Bad Match), and adopting LLM training methodologies (e.g., scaling laws, transfer learning, and knowledge distillation). Furthermore, research focuses on unified architectures for search and recommendation systems, such as LinkedIn's 360Brew and Netflix's UniCoRn, to improve efficiency and performance. Overall, these studies demonstrate the significant potential of LLMs in enhancing recommendation systems and search engines, yielding substantial real-world results.