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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Multigraph Convolutional Network Enhanced Neural Factorization Machine for Service Recommendation
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Trust-aware denoising autoencoder with spatial-temporal activity for cross-domain personalized recommendations
TL;DR: In this article , the authors proposed a cross-domain recommendation system that not only takes into account the time at finer granularity levels (e.g., hours, days, weeks, etc.), but also considers a persons location, trust level, and sentiment analysis while computing recommendations.
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Tele-Knowledge Pre-training for Fault Analysis
Zhuo Chen,Wen Zhang,Yufen Huang,Mingyang Chen,Yuxia Geng,Hongtao Yu,Zhen Bi,Yichi Zhang,Zhen Yao,Wenting Song,Xinliang Wu,Yi Yang,Song Jiang,Zhaoyang Lian,Ying Li,Hua-zeng Chen +15 more
TL;DR: A tele-domain pretraining model KTeleberT and its knowledge-enhanced version KTeleBERT, which includes effective prompt hints, adaptive numerical data encoding, and two knowledge injection paradigms is proposed, demonstrating the effectiveness of pre-trained KTeleBerT as a model containing diverse tele-knowledge.
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Hypercomplex Graph Collaborative Filtering
TL;DR: HyperComplex Graph Collaborative Filtering (HCGCF) as mentioned in this paper uses the Cayley-Dickson construction to define high-dimensional hypercomplex algebras and their mathematical operations.
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