Unleash the Power of Context: Enhancing Large-Scale Recommender Systems with Context-Based Prediction Models

Author:

Hartman Jan1ORCID,Klein Assaf2ORCID,Kopic Davorin3ORCID,Silberstein Natalia2ORCID

Affiliation:

1. Recommendations, Outbrain, Slovenia

2. Outbrain, Israel

3. Outbrain, Slovenia

Publisher

ACM

Reference12 articles.

1. Rohan Anil , Sandra Gadanho , Da Huang , Nijith Jacob , Zhuoshu Li , Dong Lin , Todd Phillips , Cristina Pop , Kevin Regan , Gil  I Shamir , 2022 . On the Factory Floor: ML Engineering for Industrial-Scale Ads Recommendation Models . In Proceedings of the 5th Workshop on Online Recommender Systems and User Modeling co-located with the 16th ACM Conference on Recommender Systems. Rohan Anil, Sandra Gadanho, Da Huang, Nijith Jacob, Zhuoshu Li, Dong Lin, Todd Phillips, Cristina Pop, Kevin Regan, Gil I Shamir, 2022. On the Factory Floor: ML Engineering for Industrial-Scale Ads Recommendation Models. In Proceedings of the 5th Workshop on Online Recommender Systems and User Modeling co-located with the 16th ACM Conference on Recommender Systems.

2. Huifeng Guo Ruiming Tang Yunming Ye Zhenguo Li and Xiuqiang He. 2017. DeepFM: a factorization-machine based neural network for CTR prediction. arXiv preprint arXiv:1703.04247 (2017). Huifeng Guo Ruiming Tang Yunming Ye Zhenguo Li and Xiuqiang He. 2017. DeepFM: a factorization-machine based neural network for CTR prediction. arXiv preprint arXiv:1703.04247 (2017).

3. Scaling TensorFlow to 300 million predictions per second

4. Exploration with Model Uncertainty at Extreme Scale in Real-Time Bidding

5. Practical Lessons from Predicting Clicks on Ads at Facebook

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