Difference between revisions of "2017 DeepFMAFactorizationMachinebase"

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* 3. Junxuan Chen, Baigui Sun, Hao Li, Hongtao Lu, Xian-Sheng Hua, Deep CTR Prediction in Display Advertising, Proceedings of the 2016 ACM on Multimedia Conference, October 15-19, 2016, Amsterdam, The Netherlands
 
* 3. Junxuan Chen, Baigui Sun, Hao Li, Hongtao Lu, Xian-Sheng Hua, Deep CTR Prediction in Display Advertising, Proceedings of the 2016 ACM on Multimedia Conference, October 15-19, 2016, Amsterdam, The Netherlands
 
* 4. Heng-Tze Cheng, Levent Koc, Jeremiah Harmsen, Tal Shaked, Tushar Chandra, Hrishi Aradhye, Glen Anderson, Greg Corrado, Wei Chai, Mustafa Ispir, Rohan Anil, Zakaria Haque, Lichan Hong, Vihan Jain, Xiaobing Liu, and Hemal Shah. Wide & Deep Learning for Recommender Systems. <i>CoRR</i>, Abs/1606.07792, 2016.
 
* 4. Heng-Tze Cheng, Levent Koc, Jeremiah Harmsen, Tal Shaked, Tushar Chandra, Hrishi Aradhye, Glen Anderson, Greg Corrado, Wei Chai, Mustafa Ispir, Rohan Anil, Zakaria Haque, Lichan Hong, Vihan Jain, Xiaobing Liu, and Hemal Shah. Wide & Deep Learning for Recommender Systems. <i>CoRR</i>, Abs/1606.07792, 2016.
* 5. Paul Covington, Jay Adams, Emre Sargin, Deep Neural Networks for YouTube Recommendations, Proceedings of the 10th ACM Conference on Recommender Systems, September 15-19, 2016, Boston, Massachusetts, USA
+
* 5. [[Paul Covington]], Jay Adams, Emre Sargin, Deep Neural Networks for YouTube Recommendations, Proceedings of the 10th ACM Conference on Recommender Systems, September 15-19, 2016, Boston, Massachusetts, USA
 
* 6. Thore Graepel, Joaquin Quiñonero Candela, Thomas Borchert, Ralf Herbrich, Web-scale Bayesian Click-through Rate Prediction for Sponsored Search Advertising in Microsoft's Bing Search Engine, Proceedings of the 27th International Conference on International Conference on Machine Learning, p.13-20, June 21-24, 2010, Haifa, Israel
 
* 6. Thore Graepel, Joaquin Quiñonero Candela, Thomas Borchert, Ralf Herbrich, Web-scale Bayesian Click-through Rate Prediction for Sponsored Search Advertising in Microsoft's Bing Search Engine, Proceedings of the 27th International Conference on International Conference on Machine Learning, p.13-20, June 21-24, 2010, Haifa, Israel
 
* 7. Xinran He, Junfeng Pan, Ou Jin, Tianbing Xu, Bo Liu, Tao Xu, Yanxin Shi, Antoine Atallah, Ralf Herbrich, Stuart Bowers, Joaquin Quiñonero Candela, Practical Lessons from Predicting Clicks on Ads at Facebook, Proceedings of the Eighth International Workshop on Data Mining for Online Advertising, p.1-9, August 24-27, 2014, New York, NY, USA
 
* 7. Xinran He, Junfeng Pan, Ou Jin, Tianbing Xu, Bo Liu, Tao Xu, Yanxin Shi, Antoine Atallah, Ralf Herbrich, Stuart Bowers, Joaquin Quiñonero Candela, Practical Lessons from Predicting Clicks on Ads at Facebook, Proceedings of the Eighth International Workshop on Data Mining for Online Advertising, p.1-9, August 24-27, 2014, New York, NY, USA

Revision as of 22:37, 26 March 2020

Subject Headings: DeepFM, Neural Recommender Algorithm, CTR Prediction Algorithm.

Notes

Cited By


Quotes

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.

1. Introduction

...

4 Related Work

In this paper, a new deep neural network is proposed for CTR prediction. The most related domains are CTR prediction and deep learning in recommender system. In this section, we discuss related work in these two domains.

CTR prediction plays an important role in recommender system (Richardson et al., 2007; Juan et al., 2016; McMahan et al., 2013). Besides generalized linear models and FM, a few other models are proposed for CTR prediction, such as tree-based model (He et al., 2014), tensor based model (Rendle and Schmidt-Thieme, 2010), support vector machine (Chang et al., 2010), and bayesian model (Graepel et al., 2010).

The other related domain is deep learning in recommender systems. In Section 1 and Section 2.2, several deep learning models for CTR prediction are already mentioned, thus we do not discuss about them here. Several deep learning models are proposed in recommendation tasks other than CTR prediction (e.g., [ Covington et al., 2016; Salakhutdinov et al., 2007; van den Oord et al., 2013; Wu et al., 2016; Zheng et al., 2016; Wu et al., 2017; Zheng et al., 2017 ]). (Salakhutdinov et al., 2007; Sedhain et al., 2015; Wang et al., 2015) propose to improve Collaborative Filtering via deep learning. The authors of ( Wang and Wang, 2014; van den Oord et al., 2013) extract content feature by deep learning to improve the performance of music recommendation. (Chen et al., 2016) devises a deep learning network to consider both image feature and basic feature of display adverting. (Covington et al., 2016) develops a two-stage deep learning framework for YouTube video recommendation.

The other related domain is deep learning in recommender systems. In Section 1 and Section 2.2, several deep learning models for CTR prediction are already mentioned, thus we do not discuss about them here. Several deep learning models are proposed in recommendation tasks other than CTR prediction (e.g., [Covington et al., 2016; Salakhutdinov et al., 2007; van den Oord et al., 2013; Wu et al., 2016; Zheng et al., 2016; Wu et al., 2017; Zheng et al., 2017]). [Salakhutdinov et al., 2007; Sedhain et al., 2015; Wang et al., 2015] propose to improve Collaborative Filtering via deep learning. The authors of [Wang andWang, 2014; van den Oord et al., 2013] extract content feature by deep learning to improve the performance of music recommendation. [Chen et al., 2016] devises a deep learning network to consider both image feature and basic feature of display adverting. [Covington et al., 2016] develops a two-stage deep learning framework for YouTube video recommendation.

5 Conclusions

In this paper, we proposed DeepFM, a Factorization-Machine based Neural Network for CTR prediction, to overcome the shortcomings of the state-of-the-art models and to achieve better performance. DeepFM trains a deep component and an FM component jointly. It gains performance improvement from these advantages: 1) it does not need any pre-training; 2) it learns both high- and low-order feature interactions; 3) it introduces a sharing strategy of feature embedding to avoid feature engineering. We conducted extensive experiments on two real-world datasets (Criteo dataset and a commercial App Store dataset) to compare the effectiveness and efficiency of DeepFM and the state-of-the-art models. Our experiment results demonstrate that 1) DeepFM outperforms the state-ofthe- art models in terms of AUC and Logloss on both datasets; 2) The efficiency of DeepFM is comparable to the most efficient deep model in the state-of-the-art.

There are two interesting directions for future study. One is exploring some strategies (such as introducing pooling layers) to strengthen the ability of learning most useful highorder feature interactions. The other is to train DeepFM on a GPU cluster for large-scale problems.

References

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  • 4. Heng-Tze Cheng, Levent Koc, Jeremiah Harmsen, Tal Shaked, Tushar Chandra, Hrishi Aradhye, Glen Anderson, Greg Corrado, Wei Chai, Mustafa Ispir, Rohan Anil, Zakaria Haque, Lichan Hong, Vihan Jain, Xiaobing Liu, and Hemal Shah. Wide & Deep Learning for Recommender Systems. CoRR, Abs/1606.07792, 2016.
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 AuthorvolumeDate ValuetitletypejournaltitleUrldoinoteyear
2017 DeepFMAFactorizationMachinebaseHuifeng Guo
Ruiming Tang
Yunming Ye
Zhenguo Li
Xiuqiang He
DeepFM: A Factorization-machine based Neural Network for CTR Prediction2017
AuthorHuifeng Guo +, Ruiming Tang +, Yunming Ye +, Zhenguo Li + and Xiuqiang He +
titleDeepFM: A Factorization-machine based Neural Network for CTR Prediction +
year2017 +