This repository serves as a curated collection of academic papers, tools, and frameworks related to recommender systems. It aims to provide researchers and practitioners with a centralized resource for understanding key concepts, algorithms, and practical implementations in the field. The collection covers foundational conferences and highlights specific challenges like cold-start and the application of deep learning.

How It Works

The repository organizes information by key topics and challenges within recommender systems, such as "Cold Start" and various "Deep Learning" sub-categories. It lists relevant academic papers and provides brief descriptions of recommender system engines and algorithm frameworks, including popular libraries like Surprise, LightFM, and deep learning-based approaches. This structured approach facilitates targeted learning and exploration of specific areas within recommender system research and development.

Highlighted Details

• Extensive listing of papers categorized by topic, including Cold Start and multiple Deep Learning themes.
• Curated list of recommender system engines and algorithm frameworks with brief descriptions.
• References to major conferences in data mining, machine learning, and information retrieval.

Maintenance & Community

This repository appears to be a personal collection, with no explicit mention of maintainers, community channels, or ongoing development efforts.

Licensing & Compatibility

The repository itself does not specify a license. The included papers are subject to their respective copyright and publication licenses.

Limitations & Caveats

This repository is a static collection of links and descriptions; it does not provide runnable code, benchmarks, or direct access to the papers themselves. The content's currency and completeness are dependent on the curator's updates.