Update README.md

README.md

@@ -1,6 +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. The datasets are extracted and processed from original public sources that will be introduced in the following, and are only used in our research study in ISP. Please kindly refrain from distributing the 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. 
+
+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 datasets are extracted and processed from their original public sources for research purposes as detailed below.
 
 ## Overview of Datasets
 The datasets include:
@@ -9,13 +13,12 @@ The datasets include:
 
 * [**MovieLens datasets**](movie_recommenders_data/) for movie recommenders. Two datasets are randomly selected from the source data [MovieLens Movie Ratings](https://dl.acm.org/doi/10.1145/2827872), a small version with 1000 items and a large version with 10,000 items. For movie recommendations, there are 19 different genres (e.g. action, comedy, drama, romance), 587 different producers, 34 different languages (e.g. English, French, Mandarin), and genre-language pairs. This leads to 473 and 1,011 unique movie labels for the small and large datasets, respectively.
 
-Each dataset in GoodReads and MovieLens contains a `*_data.csv` file, which contain the text content (i.e., title + description) of the items, and a `*_label.csv`, which contains the labels (e.g., genre or language) and a binary 0/1 denoting whether an item exbihits a label. 
+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. 
 
 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.
 
-[Selective](https://github.com/fidelity/selective) implements the multi-objective optimization approach from ([CPAIOR'21](https://link.springer.com/chapter/10.1007/978-3-030-78230-6_27), [DSO@IJCAI'22](https://arxiv.org/abs/2112.03105)) 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.
 
 ## Quick Start
 To run the example, install required packages by `pip install selective datasets`
@@ -26,7 +29,6 @@ from datasets import load_dataset
 from textwiser import TextWiser, Embedding, Transformation
 from feature.selector import Selective, SelectionMethod
 
-
 # Load Text Contents
 data = load_dataset('skadio/optimized_item_selection', data_files='book_recommenders_data/goodreads_1k_data.csv', split='train')
 data = data.to_pandas()
@@ -39,9 +41,10 @@ labels.set_index('label', inplace=True)
 # TextWiser featurization method to create text embeddings
 textwiser = TextWiser(Embedding.TfIdf(), Transformation.NMF(n_components=20, random_state=1234))
 
-# Text-based selection with the default selection method - Multi-Level Optimization that maximizes 
-# coverage and diversity within an upper bound on subset size [CPAIOR'21, DSO@IJCAI'22], by choosing 
-# the default configuration, i.e. optimization_method="exact" and cost_metric ="diverse".
+# Text-based selection with the default configuration
+# The default configuration is optimization_method="exact" and cost_metric ="diverse"
+# By default, multi-level optimization maximizes coverage and diversity as described in (CPAIOR'21, DSO@IJCAI'22)
+# within an upper bound on subset size given as num_features
 selector = Selective(SelectionMethod.TextBased(num_features=30, featurization_method=textwiser))
 
 # Result
@@ -49,8 +52,11 @@ subset = selector.fit_transform(data, labels)
 print("Reduction:", list(subset.columns))
 ```
 ## Advanced Usages
-Text-based Selection provides access to multiple selection methods. The following configurations are 
-available to apply these methods:
+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.
+
+### Exact
 
 - (Default) Solve for Problem *P_max_cover@t* in **CPAIOR'21** - Selecting a subset of items that 
 maximizes coverage of labels and maximizes the diversity in latent embedding space within an upper 
@@ -81,8 +87,11 @@ selector = Selective(SelectionMethod.TextBased(num_features=30,
                                                optimization_method='exact', 
                                                cost_metric='unicost'))
 ```
+
+### Randomized
+
 - Selecting a subset by performing random selection. If num_features is not set, subset size is defined 
-by solving #2.
+by solving *P_unicost*.
 ```python
 selector = Selective(SelectionMethod.TextBased(num_features=None, optimization_method='random'))
 ```
@@ -91,9 +100,12 @@ selector = Selective(SelectionMethod.TextBased(num_features=None, optimization_m
 selector = Selective(SelectionMethod.TextBased(num_features=30, 
                                                optimization_method='random'))
 ```
+
+### Greedy
+
 - Selecting a subset by adding an item each time using a greedy heuristic in selection with a given
 cost_metric, i.e. `diverse` by default or `unicost`. If num_features is not set, subset size is defined 
-by solving #2.
+by solving *P_unicost*.
 ```python
 selector = Selective(SelectionMethod.TextBased(num_features=None, 
                                                optimization_method='greedy',
@@ -106,8 +118,11 @@ selector = Selective(SelectionMethod.TextBased(num_features=30,
                                                optimization_method='greedy',
                                                cost_metric='unicost'))
 ```
+
+### Clustering
+
 - Selecting a subset by clustering items into a number of clusters and the items close to the centroids 
-are selected. If num_features is not set, subset size is defined by solving #2. `cost_metric` argument 
+are selected. If num_features is not set, subset size is defined by solving *P_unicost*. `cost_metric` argument 
 is not used in this method.
 ```python
 selector = Selective(SelectionMethod.TextBased(num_features=None, optimization_method='kmeans'))