This repository compiles essential knowledge points and papers for Search and Recommendation Algorithm Engineer interviews. It serves as a personal summary for interview preparation, covering a broad spectrum of topics from deep learning fundamentals to statistical machine learning, NLP, and recommendation system specifics.

How It Works

The project is structured as a curated collection of notes and references, organized by topic. It aims to be comprehensive, covering foundational concepts, advanced models, and practical considerations relevant to the field. The approach is to provide a consolidated resource for self-study and review, rather than a runnable codebase.

Highlighted Details

• Covers deep learning (BatchNorm, Seq2seq, CNN, RNN, GAN, optimizers, loss functions) and statistical ML (linear models, SVM, trees, clustering, PCA, entropy).
• Includes extensive NLP topics (Word2vec, LSA, GloVe, FastText, ELMo, Transformers, BERT, GPT) and recommendation system concepts (CTR models, recall, interpretability, graph networks, knowledge distillation, learning-to-rank).
• Features specific models and techniques like SimCSE, ColBERT, BM25, Faiss, PageRank, and Airbnb's data-driven models.
• References external resources and notes from other contributors for broader coverage.

Maintenance & Community

This is a personal project, and maintenance status is not explicitly detailed. Community interaction channels are not provided.

Licensing & Compatibility

The repository does not specify a license. Compatibility for commercial use or closed-source linking is not addressed.

Limitations & Caveats

The content is a personal summary and may not be universally understandable. Some sections, such as "Distributed Databases" and "Computer Science Fundamentals," are marked as incomplete.