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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Accelerating matrix factorization by overparameterization
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TL;DR: It is found that overparameterization can accelerate the optimization of MF with no change in the expressiveness of the learning model, and modern applications on recommendations based on MF or its variants can largely benefit from this discovery.
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Multi-view Graph Attention Network for Travel Recommendation
TL;DR: Wang et al. as discussed by the authors proposed a multi-view graph attention network (MV-GAN), which enriches user and product semantics through both metapath-guided neighbors aggregation and multiview fusion in heterogeneous travel product recommendation graph.
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Distributed Equivalent Substitution Training for Large-Scale Recommender Systems
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TL;DR: Distributed Equivalent Substitution (DES) as discussed by the authors replaces weights-rich operators with the computationally equivalent sub-operators and aggregates partial results instead of transmitting the huge sparse weights directly through the network.
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LinkLive: discovering Web learning resources for developers from Q&A discussions
Jing Li,Zhenchang Xing,Aixin Sun +2 more
TL;DR: This paper presents an item-based collaborative filtering technique, named LinkLive, for automatically recommending a list of correlated Web resources for a particular Web page by exploiting hyperlink associations from the crowdsourced knowledge on Stack Overflow.
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EEG Signal and Feature Interaction Modeling-Based Eye Behavior Prediction Research.
Pengcheng Ma,Qian Gao +1 more
TL;DR: The innovation of this paper is to analyze the EEG signal for the first time by building a depth factorization machine model, so that on the basis of analyzing the characteristics of user interaction, EEG data can be used to predict the binomial state of eyes.
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