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
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HIEN: Hierarchical Intention Embedding Network for Click-Through Rate Prediction
TL;DR: A novel approach Hierarchical Intention Embedding Network (HIEN), which considers dependencies of attributes based on bottom-up tree aggregation in the constructed attribute graph and captures user intents for different item attributes as well as item intents based on the proposed hierarchical attention mechanism.
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Click-through rate prediction using transfer learning with fine-tuned parameters
TL;DR: In this paper , the authors propose an end-to-end transfer learning framework with fine-tuned parameters for CTR prediction, called Automatic Fine-Tuning (AutoFT), which is a set of learnable transfer policies that independently determine how the specific instance-based fine-tuning policies should be trained, which decide the routing in the embedding representations and the high-order feature representations layer by layer in deep CTR model.
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Hierarchically Fusing Long and Short-Term User Interests for Click-Through Rate Prediction in Product Search
TL;DR: A new approach named Hierarchical Interests Fusing Network (HIFN), which consists of four basic modules namely Short-term Interest Extractor (SIE), Long-term interest extractor (LIE), Interest Fusion Module (IFM) and Interest Disentanglement Module (IDM), which is proposed to resolve challenges in personalized product search.
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