license: apache-2.0
tags:
- optimized item selection
- recommender systems
- online experimentation
- multi-objective optimization
pretty_name: ISP
Optimized Item Selection Datasets
We provide the datasets that are used to test the multi-level optimization framework (CPAIOR'21, DSO@IJCAI'22), for solving Item Selection Problem (ISP) to boost exploration in Recommender Systems.
Overview of Datasets
The datasets include:
GoodReads datasets for book recommenders. Two datasets are randomly selected from the source data GoodReads Book Reviews, 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 for movie recommenders. Two datasets are randomly selected from the source data MovieLens Movie Ratings, 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 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 implements the multi-objective optimization approach from (CPAIOR'21, DSO@IJCAI'22) 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.
Usage Example
# Import Selective (for text-based selection) and TextWiser (for embedding space)
import pandas as pd
from feature.selector import Selective, SelectionMethod
from textwiser import TextWiser, Embedding, Transformation
# Load Text Contents
data = pd.read_csv("goodreads_1k_data.csv").astype(str)
# Load Labels
labels = pd.read_csv("goodreads_1k_label.csv")
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
selector = Selective(SelectionMethod.TextBased(num_features=30, featurization_method=textwiser))
# Result
subset = selector.fit_transform(data, labels)
print("Reduction:", list(subset.columns))
Citation
If you use ISP in our research/applications, please cite as follows: