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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A Deep Prediction Network for Understanding Advertiser Intent and Satisfaction
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TL;DR: Wang et al. as mentioned in this paper proposed a Deep Satisfaction Prediction Network (DSPN), which jointly learns advertiser intent vector and satisfaction by considering the features of advertiser's action information and advertising performance indicators.
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AIRC: Attentive Implicit Relation Recommendation Incorporating Content Information for Bipartite Graphs
TL;DR: This paper proposes the attentive implicit relation recommendation incorporating content information (AIRC) framework that is designed for bipartite graphs based on the GC–MC algorithm and shows that the framework performs better than other state-of-art recommendation algorithms.
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Addressing Confounding Feature Issue for Causal Recommendation
TL;DR: A framework named Deconfounding Causal Recommendation (DCR), which performs intervened inference with do-calculus and a mixture-of-experts (MoE) model architecture, modeling each value of confounding feature with a separate expert module, showing that DCR leads to more accurate prediction of user preference with small inference time cost.
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Adaptive Spatiotemporal Dependence Learning for Multi-Mode Transportation Demand Prediction
TL;DR: Wang et al. as mentioned in this paper proposed a co-modal graph attention framework to uncover the impact of different spatial relationships and traffic mode interactions on traffic demand, where a multiple traffic graphs-based spatial attention mechanism and a multiple time periods-based temporal attention mechanism are proposed to capture spatial and temporal dependencies in multi-mode traffic demands.
Posted Content
Privacy-Aware Recommender Systems Challenge on Twitter's Home Timeline
Luca Belli,Sofia Ira Ktena,Alykhan Tejani,Alexandre Lung-Yut-Fon,Frank Portman,Xiao Zhu,Yuanpu Xie,Akshay Gupta,Michael M. Bronstein,Amra Delic,Gabriele Sottocornola,Walter Anelli,Nazareno Andrade,Jessie Smith,Wenzhe Shi +14 more
TL;DR: The key challenges faced by researchers and professionals striving to predict user engagements are touched on, and the key aspects of the RecSys 2020 Challenge that was organized by ACM RecSys in partnership with Twitter using this dataset are described.
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