DeepFM: a factorization-machine based neural network for CTR prediction
Huifeng Guo,Ruiming Tang,Yunming Ye,Zhenguo Li,Xiuqiang He +4 more
- pp 1725-1731
TLDR
This paper shows that it is possible to derive an end-to-end learning model that emphasizes both low- and high-order feature interactions, and combines the power of factorization machines for recommendation and deep learning for feature learning in a new neural network architecture.Abstract:
Learning sophisticated feature interactions behind user behaviors is critical in maximizing CTR for recommender systems. Despite great progress, existing methods seem to have a strong bias towards low- or high-order interactions, or require expertise feature engineering. In this paper, we show that it is possible to derive an end-to-end learning model that emphasizes both low- and high-order feature interactions. The proposed model, DeepFM, combines the power of factorization machines for recommendation and deep learning for feature learning in a new neural network architecture. Compared to the latest Wide & Deep model from Google, DeepFM has a shared input to its "wide" and "deep" parts, with no need of feature engineering besides raw features. Comprehensive experiments are conducted to demonstrate the effectiveness and efficiency of DeepFM over the existing models for CTR prediction, on both benchmark data and commercial data.read more
Citations
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Deep Learning and Embedding Based Latent Factor Model for Collaborative Recommender Systems
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AutoAssign: Automatic Shared Embedding Assignment in Streaming Recommendation
TL;DR: In this paper , a reinforcement learning-based automatic shared embedding assignment framework, AutoAssign, is proposed for streaming recommender systems, where an identity agent serves to field-wise represent low-frequency IDs by utilizing a small number of shared embeddings and dynamically identify the ID features that need to be retained or eliminated in the embedding table.
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A Survey of Artificial Intelligence-Based E-Commerce Recommendation System
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TL;DR: Wang et al. as mentioned in this paper proposed a variational bandwidth auto-encoder (VBAE) for recommendation, which first encodes user collaborative and feature information into Gaussian latent variables via deep neural networks to capture non-linear user similarities.
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