datawhalechina/fun-rec

Features

• Generative Recommendation Models - Implements a generative paradigm where large language models generate item identifiers directly as a sequence-to-sequence task.
• Generative - Implements generative recommendation paradigms using LLMs and diffusion models to generate item suggestions directly.
• Sequence Interaction Modeling - Combines historical user behavior with cross-feature interactions to predict the probability of future engagement.
• Candidate Generation - Optimizes the extraction of potential items from large datasets using vector embeddings and collaborative filtering.
• Embedding-Based Retrieval - Employs vector representations and similarity searches to efficiently retrieve candidate items from large databases.
• Recommendation Engine Pipelines - Coordinates the full recommendation lifecycle from feature engineering and model training to real-time online serving.
• Personalized Recommendation Retrieval - Identifies potential item matches by filtering large datasets through collaborative filtering and embedding-based retrieval.
• Preference Prediction - Predicts user preferences and the likelihood of engagement using feature crossing and sequence modeling.
• Recommendation Architectures - Provides a framework for constructing personalized content architectures using deep learning, cascade models, and generative paradigms.
• Generative - Provides architectural patterns for replacing traditional ranking lists with end-to-end item generation using transformers.
• Generative Recommendation Modeling - Generates item suggestions instead of ranking lists using large language models and diffusion models.
• Recommendation Pipelines - Builds end-to-end infrastructure spanning offline data pipelines, online serving, and deployment operations.
• Recommender Systems - Integrates offline pipelines and online serving processes into a complete industrial-grade recommendation architecture.
• Generative Item Suggestion - Creates end-to-end item suggestions using large language models and specialized tokenizers.
• Candidate Retrieval APIs - Implements candidate retrieval mechanisms using vector representations and similarity searches to narrow the search space.
• Deep Learning Tutorials - Offers instructional content on predicting preferences through collaborative filtering and deep factorized models.
• Generative Recommendation Tutorials - Serves as a learning guide for building personalized content suggestion systems using LLMs.
• Cascade Recommendation Architectures - Implements a multi-stage cascade architecture to filter massive item pools through retrieval and ranking stages.
• Hybrid Offline-Online Recommendation Pipelines - Provides an industrial-grade architecture separating heavy offline model training from real-time online serving for low latency.
• Item-to-Item Similarity - Identifies potential items for users by calculating similarity between items for related content suggestions.
• Diffusion-Based Augmentations - Uses diffusion-based noise and denoising processes to enhance training sequences and model robustness.
• Greedy Re-Ranking Algorithms - Applies greedy search algorithms to the final item list to maximize diversity and prevent repetitive content.
• Diversity Optimization Strategies - Implements methods for optimizing list variety using greedy algorithms and re-ranking models.
• List Diversification - Applies re-ranking algorithms to balance relevance with variety and prevent information silos.
• Result Diversification - Prevents information silos by reordering items to break continuous sequences of the same category.
• Cold Start Solvers - Provides relevant suggestions for new users or items by balancing exploration and exploitation algorithms.
• Diffusion-Based - Utilizes denoising diffusion models to generate synthetic training sequences for improved model robustness.
• Probability-Based Ranking - Sort candidates by predicting the likelihood of a user clicking an item using deep factorized models.
• Recommendation Reasoning - Aligns item indices using linguistic semantics and reasoning frameworks to improve the logic behind content suggestions.
• Diversity-Aware - Balances relevance with variety by re-ranking items using greedy algorithms or transformer models.

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