Update README.md

README.md

@@ -1,10 +1,10 @@
 # Optimized Item Selection Datasets
 
-We provide the datasets that are used to test the multi-level optimization framework ([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. 
+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. 
 
-The 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.
+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.
 
-The datasets are extracted and processed from their original public sources for research purposes as detailed below.
+The datasets are extracted and processed from their original public sources for research purposes, as detailed below.
 
 ## Overview of Datasets
 The datasets include:
@@ -15,13 +15,13 @@ The datasets include:
 
 Each dataset in GoodReads and MovieLens contains:
 * `*_data.csv` that contains the text content (i.e., title + description) of the items, and
-* `*_label.csv` that contains the labels (e.g., genre or language) and a binary 0/1 value denoting whether an item exbihits a label. 
+* `*_label.csv` that contains the labels (e.g., genre or language) and a binary 0/1 value denoting whether an item exhibits a label. 
 
-Each column in the csv file is for an item, indexed by book/movie ID. The order of columns in data and label files are the same.
+Each column in the csv file is for an item indexed by book/movie ID. The order of columns in data and label files are the same.
 
 
 ## Quick Start
-To run the example, install required packages by `pip install selective datasets`
+To run the example, install the required packages by `pip install selective datasets`.
 ```python
 # Import Selective (for text-based selection) and TextWiser (for embedding space)
 import pandas as pd
@@ -54,7 +54,7 @@ print("Reduction:", list(subset.columns))
 ## Advanced Usages
 Text-based Selection provides access to multiple selection methods. 
 
-At a high-level, the configurations can be divided into exact, randomized, greedy or cluster-based optimization.
+At a high level, the configurations can be divided into exact, randomized, greedy, or cluster-based optimization.
 
 ### Exact
 
@@ -81,7 +81,7 @@ selector = Selective(SelectionMethod.TextBased(num_features=None,
                                                optimization_method='exact', 
                                                cost_metric='diverse'))
 ```
-- Selecting a subset of items that only maximizes coverage within an upper bound on subset size.
+- Selecting a subset of items that only maximize coverage within an upper bound on subset size.
 ```python
 selector = Selective(SelectionMethod.TextBased(num_features=30, 
                                                optimization_method='exact', 
@@ -136,6 +136,19 @@ selector = Selective(SelectionMethod.TextBased(num_features=30,
 
 ## Citation
 If you use ISP in our research/applications, please cite as follows:
+
+@article{amai2024,
+  title        = {Integrating optimized item selection with active learning for continuous exploration in recommender systems},
+  author       = {Serdar Kadioglu and Bernard Kleynhans and Xin Wang},
+  journal      = {Ann. Math. Artif. Intell.},
+  volume       = {92},
+  number       = {6},
+  pages        = {1585--1607},
+  year         = {2024},
+  url          = {https://doi.org/10.1007/s10472-024-09941-x},
+  doi          = {10.1007/S10472-024-09941-X}
+}
+
 ```bibtex
   @inproceedings{cpaior2021,
     title={Optimized Item Selection to Boost Exploration for Recommender Systems},