Welcome to "NLP Recommenders," a repository dedicated to exploring the intersection of Recommender Systems and Natural Language Processing (NLP). This evolving collection features a range of projects and explorations, starting from the basics of Recommender Systems and gradually integrating NLP techniques to augment their capabilities. We begin with foundational concepts and methods in recommender systems, then transition into incorporating NLP, showcasing how these technologies can work in tandem to create more intelligent and nuanced systems.
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Introduction to Recommender Systems:
- A deep dive into various methods of recommender systems and foundational approaches to building them.
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Python Tools for Building Recommender Systems:
- A curated list of essential Python tools for constructing efficient and effective recommender systems.
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Mock Dataset Development for LightFM Example:
- Step-by-step guide on creating a mock dataset specifically designed for use with the LightFM collaborative filtering model.
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Collaborative Filtering using LightFM:
- Detailed exploration of collaborative filtering, including an example focused on recommending educational tasks of appropriate difficulty levels to students.
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Mock Dataset Development for PyTorch Recommender Example:
- Guidance on developing a mock dataset tailored for a recommender system implemented in PyTorch.
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Recommender Systems in PyTorch:
- Demonstrates the construction of a recommender system in PyTorch, aimed at predicting optimal study time allocation for students across various content types.
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Introduction to Natural Language Processing:
- Covers general methods and applications of NLP, with a focus on educational tool applications.
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Using the ChatGPT API to Develop a Mock Dataset:
- Instructions and insights on utilizing the ChatGPT API to create a mock dataset for NLP applications.
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- Step-by-step tutorial on developing an NLP model in PyTorch capable of predicting the grade level of a given text piece.