# Data Mining Hackathon on BIG DATA (7GB) Best Buy mobile web site — Data

来源：https://www.kaggle.com/competitions/acm-sf-chapter-hackathon-big/data

采集时间：2026-10-19T07:42:51Z

The main data for this competition is in the
train.csv
and
test.csv
files. These files contain information on what items users clicked on after making a search.
Each line of
train.csv
describes a user's click on a single item. It contains the following fields:
user: A user ID
sku: The stock-keeping unit (item) that the user clicked on
category: The category the sku belongs to
query: The search terms that the user entered
click_time: Time the sku was clicked on
query_time: Time the query was run
test.csv
contains all of the same fields as train.csv except for sku. It is your job to estimate which sku's were clicked on in these test queries.
Due to the internal structure of BestBuy's databases, there is no guarantee that the user clicks resulted from a search with the given query. What we do know is that the user made a query at query_time, and then, at click_time, they clicked on the sku, but
 we don't know that the click came from the search results. The click_time is never more than five minutes after the query_time.
In addition, there is information about products, product categories, and product reviews in
product_data.tar.gz
.
We have also provided a sample benchmark submission and the code that produces it.
popular_skus.py
is a simple python script that finds the most popular skus in each product category, and then estimates that a user clicked on one of the five most popular skus in their product category. This script produces the benchmark in
popular_skus.csv
.
The syntax of a submission should be the same as that in popular_skus.csv: A file with the header "sku", and each of the following lines containing the space-delimited estimates of the clicked sku that resulted after the queries in test.csv, and in the same
 order.
Product Data Dictionary
[https://bbyopen.com/documentation/products-api/product-attributes#TableProdRefInfo](https://bbyopen.com/documentation/products-api/product-attributes#TableProdRefInfo)
For more BestBuy Data and APIs, check out
[https://bbyopen.com/](https://bbyopen.com/)
and more BigDataR tools
[https://github.com/koooee/BigDataR_Examples/tree/master /ACM_comp](https://github.com/koooee/BigDataR_Examples/tree/master/ACM_comp)