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  # Optimized Item Selection Datasets
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- We provide the datasets that are used to test the multi-level optimization framework ([AMAI'24](https://link.springer.com/epdf/10.1007/s10472-024-09941-x?sharing_token=9XBJ6cdglsdji19gFwuqQve4RwlQNchNByi7wbcMAY4VwIBKydj3Ja9OBjALNpg8nuO300abjlrHmZQFBVUqar-uYhBML28cmbovFgiHRRvd7TM2QAA_Hwd5J3U2MmKx0ugXwF6yz2hW75_88JpLmXSDJSuyCEwqZqtOcB7BhJU=),[CPAIOR'21](https://link.springer.com/chapter/10.1007/978-3-030-78230-6_27), [DSO@IJCAI'22](https://arxiv.org/abs/2112.03105)), for solving Item Selection Problem (ISP) to boost exploration in Recommender Systems.
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  The multi-objective optimization framework is implemented in [Selective](https://github.com/fidelity/selective) as part of `TextBased Selection`. By solving the ISP with Text-based Selection in Selective, we select a smaller subset of items with maximum diversity in the latent embedding space of items and maximum coverage of labels.
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  ## Citation
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  If you use ISP in our research/applications, please cite as follows:
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  @article{amai2024,
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  title = {Integrating optimized item selection with active learning for continuous exploration in recommender systems},
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  author = {Serdar Kadioglu and Bernard Kleynhans and Xin Wang},
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  url = {https://doi.org/10.1007/s10472-024-09941-x},
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  doi = {10.1007/S10472-024-09941-X}
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  }
 
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  ```bibtex
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  @inproceedings{cpaior2021,
 
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  # Optimized Item Selection Datasets
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+ We provide the datasets that are used to test the multi-level optimization framework ([AMAI'24](https://link.springer.com/epdf/10.1007/s10472-024-09941-x?sharing_token=9XBJ6cdglsdji19gFwuqQve4RwlQNchNByi7wbcMAY4VwIBKydj3Ja9OBjALNpg8nuO300abjlrHmZQFBVUqar-uYhBML28cmbovFgiHRRvd7TM2QAA_Hwd5J3U2MmKx0ugXwF6yz2hW75_88JpLmXSDJSuyCEwqZqtOcB7BhJU=), [DSO@IJCAI'22](https://arxiv.org/abs/2112.03105), [CPAIOR'21](https://link.springer.com/chapter/10.1007/978-3-030-78230-6_27)) for solving Item Selection Problem (ISP) to boost exploration in Recommender Systems.
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  The multi-objective optimization framework is implemented in [Selective](https://github.com/fidelity/selective) as part of `TextBased Selection`. By solving the ISP with Text-based Selection in Selective, we select a smaller subset of items with maximum diversity in the latent embedding space of items and maximum coverage of labels.
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  ## Citation
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  If you use ISP in our research/applications, please cite as follows:
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+ ```bibtex
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  @article{amai2024,
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  title = {Integrating optimized item selection with active learning for continuous exploration in recommender systems},
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  author = {Serdar Kadioglu and Bernard Kleynhans and Xin Wang},
 
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  url = {https://doi.org/10.1007/s10472-024-09941-x},
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  doi = {10.1007/S10472-024-09941-X}
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  }
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+ ```
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  ```bibtex
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  @inproceedings{cpaior2021,