Modeling User Exposure in Recommendation
On using latent variables for exposure of an user to an item to build a better recommendation systems.
Link: https://arxiv.org/abs/1510.07025
#recommender #RS
On using latent variables for exposure of an user to an item to build a better recommendation systems.
Link: https://arxiv.org/abs/1510.07025
#recommender #RS
arXiv.org
Modeling User Exposure in Recommendation
Collaborative filtering analyzes user preferences for items (e.g., books,
movies, restaurants, academic papers) by exploiting the similarity patterns
across users. In implicit feedback settings,...
movies, restaurants, academic papers) by exploiting the similarity patterns
across users. In implicit feedback settings,...
💣New open-source recommender system from Facebook.
Facebook is open-sourcing DLRM — a state-of-the-art deep learning recommendation model to help AI researchers and the systems and hardware community develop new, more efficient ways to work with categorical data.
Link: https://ai.facebook.com/blog/dlrm-an-advanced-open-source-deep-learning-recommendation-model/
Github: https://github.com/facebookresearch/dlrm
ArXiV: https://arxiv.org/abs/1906.03109
#Facebook #DLRM #recommender #DL #PyTorch #Caffe
Facebook is open-sourcing DLRM — a state-of-the-art deep learning recommendation model to help AI researchers and the systems and hardware community develop new, more efficient ways to work with categorical data.
Link: https://ai.facebook.com/blog/dlrm-an-advanced-open-source-deep-learning-recommendation-model/
Github: https://github.com/facebookresearch/dlrm
ArXiV: https://arxiv.org/abs/1906.03109
#Facebook #DLRM #recommender #DL #PyTorch #Caffe
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RecMind: Large Language Model Powered Agent For Recommendation
Recent advancements have significantly improved the capabilities of Large Language Models (LLMs) in various tasks, yet their potential in the realm of personalized recommendations has been relatively unexplored. To address this gap, a new LLM-powered autonomous recommender agent called RecMind has been developed. RecMind is designed to provide highly personalized recommendations by leveraging planning algorithms, tapping into external data sources, and using individualized data.
One standout feature of RecMind is its novel "Self-Inspiring" algorithm, which enhances the model's planning abilities. During each step of planning, the algorithm encourages the model to consider all its past actions, thereby improving its understanding and use of historical data. The performance of RecMind has been evaluated across multiple recommendation tasks like rating prediction, sequential and direct recommendation, explanation generation, and review summarization. The results show that RecMind outperforms existing LLM-based methods in these tasks and is competitive with the specialized P5 model.
Paper link: https://arxiv.org/abs/2308.14296
A detailed unofficial overview of the paper:
https://andlukyane.com/blog/paper-review-recmind
#deeplearning #nlp #llm #recommender
Recent advancements have significantly improved the capabilities of Large Language Models (LLMs) in various tasks, yet their potential in the realm of personalized recommendations has been relatively unexplored. To address this gap, a new LLM-powered autonomous recommender agent called RecMind has been developed. RecMind is designed to provide highly personalized recommendations by leveraging planning algorithms, tapping into external data sources, and using individualized data.
One standout feature of RecMind is its novel "Self-Inspiring" algorithm, which enhances the model's planning abilities. During each step of planning, the algorithm encourages the model to consider all its past actions, thereby improving its understanding and use of historical data. The performance of RecMind has been evaluated across multiple recommendation tasks like rating prediction, sequential and direct recommendation, explanation generation, and review summarization. The results show that RecMind outperforms existing LLM-based methods in these tasks and is competitive with the specialized P5 model.
Paper link: https://arxiv.org/abs/2308.14296
A detailed unofficial overview of the paper:
https://andlukyane.com/blog/paper-review-recmind
#deeplearning #nlp #llm #recommender
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