| # Optimized Item Selection Datasets | |
| 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. | |
| 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: | |
| * [**GoodReads datasets**](book_recommenders_data/) for book recommenders. Two datasets are randomly selected from the source data [GoodReads Book Reviews](https://dl.acm.org/doi/10.1145/3240323.3240369), a small version with 1000 items and a large version with 10,000 items. For book recommendations, there are 11 different genres (e.g., fiction, non-fiction, children), 231 different publishers (e.g. Vintage, Penguin Books, Mariner Books), and genre-publisher pairs. This leads to 574 and 1,322 unique book labels for the small and large datasets, respectively. | |
| * [**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: | |
| * `*_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 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. | |
| ## Quick Start | |
| 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 | |
| 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() | |
| # Load Labels | |
| labels = load_dataset('skadio/optimized_item_selection', data_files='book_recommenders_data/goodreads_1k_label.csv', split='train') | |
| labels = labels.to_pandas() | |
| 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 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 | |
| subset = selector.fit_transform(data, labels) | |
| 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. | |
| ### 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 | |
| bound on subset size. | |
| ```python | |
| selector = Selective(SelectionMethod.TextBased(num_features=30, | |
| featurization_method=textwiser, | |
| optimization_method='exact', | |
| cost_metric='diverse')) | |
| ``` | |
| - Solve for Problem *P_unicost* in **CPAIOR'21** - Selecting a subset of items that covers all labels. | |
| ```python | |
| selector = Selective(SelectionMethod.TextBased(num_features=None, | |
| optimization_method='exact', | |
| cost_metric='unicost')) | |
| ``` | |
| - Solve for Problem *P_diverse* in **CPAIOR'21** - Selecting a subset of items with maximized diversity | |
| in the latent embedding space while still maintaining the coverage over all labels. | |
| ```python | |
| selector = Selective(SelectionMethod.TextBased(num_features=None, | |
| featurization_method=textwiser, | |
| optimization_method='exact', | |
| cost_metric='diverse')) | |
| ``` | |
| - 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', | |
| cost_metric='unicost')) | |
| ``` | |
| ### Randomized | |
| - Selecting a subset by performing random selection. If num_features is not set, subset size is defined | |
| by solving *P_unicost*. | |
| ```python | |
| selector = Selective(SelectionMethod.TextBased(num_features=None, optimization_method='random')) | |
| ``` | |
| - Selecting a subset by performing random selection. Subset size is defined by num_features. | |
| ```python | |
| 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 *P_unicost*. | |
| ```python | |
| selector = Selective(SelectionMethod.TextBased(num_features=None, | |
| optimization_method='greedy', | |
| cost_metric='unicost')) | |
| ``` | |
| - 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`. | |
| ```python | |
| 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 *P_unicost*. `cost_metric` argument | |
| is not used in this method. | |
| ```python | |
| selector = Selective(SelectionMethod.TextBased(num_features=None, optimization_method='kmeans')) | |
| ``` | |
| - Selecting a subset by clustering items into a number of clusters and the items close to the centroids | |
| are selected. `cost_metric` argument is not used in this method. | |
| ```python | |
| selector = Selective(SelectionMethod.TextBased(num_features=30, | |
| optimization_method='kmeans')) | |
| ``` | |
| ## Citation | |
| If you use ISP in our research/applications, please cite as follows: | |
| ```bibtex | |
| @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}, | |
| author={Serdar Kadıoğlu and Bernard Kleynhans and Xin Wang}, | |
| booktitle={Proceedings of Integration of Constraint Programming, Artificial Intelligence, and Operations Research: 18th International Conference, CPAIOR 2021, Vienna, Austria, July 5–8, 2021}, | |
| url={https://doi.org/10.1007/978-3-030-78230-6_27}, | |
| pages = {427–445}, | |
| year={2021} | |
| } | |
| ``` | |
| ```bibtex | |
| @inproceedings{ijcai2022, | |
| title={Active Learning Meets Optimized Item Selection}, | |
| author={Bernard Kleynhans and Xin Wang and Serdar Kadıoğlu}, | |
| booktitle={The IJCAI-22 Workshop: Data Science meets Optimisation} | |
| publisher={arXiv}, | |
| url={https://arxiv.org/abs/2112.03105}, | |
| year={2022} | |
| } | |
| ``` |