arXiv:1809.00999
공식 코드 · Papers with Code
Towards Large Scale Training Of Autoencoders For Collaborative Filtering
원문 초록
In this paper, we apply a mini-batch based negative sampling method to efficiently train a latent factor autoencoder model on large scale and sparse data for implicit feedback collaborative filtering. We compare our work against a state-of-the-art baseline model on different experimental datasets and show that this method can lead to a good and fast approximation of the baseline model performance. The source code is available in https://github.com/amoussawi/recoder .
함께 살펴볼 논문
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NLGCL: Naturally Existing Neighbor Layers Graph Contrastive Learning for Recommendation
From ID-based to ID-free: Rethinking ID Effectiveness in Multimodal Collaborative Filtering Recommendation
RAG-VisualRec: An Open Resource for Vision- and Text-Enhanced Retrieval-Augmented Generation in Recommendation
LLM2Rec: Large Language Models Are Powerful Embedding Models for Sequential Recommendation
Dual-View Disentangled Multi-Intent Learning for Enhanced Collaborative Filtering
Discrete Scale-invariant Metric Learning for Efficient Collaborative Filtering
토론
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