haritzpuerto/amazon_shopping_queries_old

This dataset is a sample for the Amazon Shopping Queries Dataset.

This dataset contains queries for which at least 10 products are available. The products if possible are exact matches to the query intent, or at least substitutes It was constructed as follows:

import pandas as pd

df_examples = pd.read_parquet("shopping_queries_dataset_examples.parquet")
df_products = pd.read_parquet("shopping_queries_dataset_products.parquet")
df_sources = pd.read_csv("shopping_queries_dataset_sources.csv")

df_examples_products = pd.merge(
    df_examples,
    df_products,
    how="left",
    left_on=["product_locale", "product_id"],
    right_on=["product_locale", "product_id"],
)

df_examples_products_source = pd.merge(
    df_examples_products,
    df_sources,
    how="left",
    left_on=["query_id"],
    right_on=["query_id"],
)

list_hits = []
for query_id in tqdm(list_query_id):
    df = retrieve_products(query_id, df_examples_products_source)
    list_len_desc = []
    for row_idx in range(len(df)):
        row = df.iloc[row_idx]
        full_description = format_product_details(row)
        list_len_desc.append(len(full_description))
    if len(df) >= 10:
        list_hits.append((df, np.mean(list_len_desc)))

# sort by length of full_description
list_hits = sorted(list_hits, key=lambda x: x[1], reverse=True)

df = pd.concat([x[0] for x in list_hits[:1000]])

The auxiliary functions are:

def format_product_details(product):
    template = "List of features:\n{features}\n\nDescription:\n{description}"
    features = product["product_bullet_point"]
    description = product["product_description"]
    return template.format(features=features, description=description)


def retrieve_products(query_id, df_examples_products_source):
    df = df_examples_products_source[
        df_examples_products_source["query_id"] == query_id
    ]
    # product_locale = en
    df = df[df["product_locale"] == "us"]
    # remove esci_label I
    df = df[df["esci_label"] != "I"]
    # remove product_description None
    df = df[df["product_description"].notnull()]
    # remove product_bullet_point None
    df = df[df["product_bullet_point"].notnull()]
    # if esci_label E > 10, use only those
    if df[df["esci_label"] == "E"].shape[0] > 10:
        df = df[df["esci_label"] == "E"]
    # if esci_label in [E, S ]> 10, use only those
    elif df[df["esci_label"].isin(["E", "S"])].shape[0] > 10:
        df = df[df["esci_label"].isin(["E", "S
    else:
        return []
    return df