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Towards Understanding The Gaps of Offline And Online Evaluation Metrics: Impact of Series vs. Movie Recommendations

Published: 08 October 2024 Publication History
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References

[1]
Ricardo Baeza-Yates, Berthier Ribeiro-Neto, 1999. Modern information retrieval. Vol. 463. ACM press New York.
[2]
Tianqi Chen and Carlos Guestrin. 2016. Xgboost: A scalable tree boosting system. In Proceedings of the 22nd acm sigkdd international conference on knowledge discovery and data mining. 785–794.
[3]
Miroslav Dudík, Dumitru Erhan, John Langford, and Lihong Li. 2014. Doubly robust policy evaluation and optimization. (2014).
[4]
Kalervo Järvelin and Jaana Kekäläinen. 2002. Cumulated gain-based evaluation of IR techniques. ACM Transactions on Information Systems (TOIS) 20, 4 (2002), 422–446.
[5]
Harald Steck. 2019. Embarrassingly shallow autoencoders for sparse data. In The World Wide Web Conference. 3251–3257.
[6]
Adith Swaminathan and Thorsten Joachims. 2015. Batch learning from logged bandit feedback through counterfactual risk minimization. The Journal of Machine Learning Research 16, 1 (2015), 1731–1755.
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David H Wolpert. 1992. Stacked generalization. Neural networks 5, 2 (1992), 241–259.

Cited By

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  • (2024)SLLIM-Rank: A Multi-Stage Item-to-Item Recommendation Model using Learning-to-Rank2024 IEEE International Conference on Big Data (BigData)10.1109/BigData62323.2024.10826085(2264-2268)Online publication date: 15-Dec-2024
  • (2024)Harnessing the Power of Graph Neural Networks for Personalized Rail Recommendations2024 IEEE International Conference on Big Data (BigData)10.1109/BigData62323.2024.10825079(2307-2313)Online publication date: 15-Dec-2024

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  1. Towards Understanding The Gaps of Offline And Online Evaluation Metrics: Impact of Series vs. Movie Recommendations

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          cover image ACM Conferences
          RecSys '24: Proceedings of the 18th ACM Conference on Recommender Systems
          October 2024
          1438 pages
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          Published: 08 October 2024

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          Author Tags

          1. XGBoost
          2. autoencoders
          3. experimentation
          4. offline evaluation

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          • (2024)SLLIM-Rank: A Multi-Stage Item-to-Item Recommendation Model using Learning-to-Rank2024 IEEE International Conference on Big Data (BigData)10.1109/BigData62323.2024.10826085(2264-2268)Online publication date: 15-Dec-2024
          • (2024)Harnessing the Power of Graph Neural Networks for Personalized Rail Recommendations2024 IEEE International Conference on Big Data (BigData)10.1109/BigData62323.2024.10825079(2307-2313)Online publication date: 15-Dec-2024

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