# Data Mining Hackathon on (20 mb) Best Buy mobile web site - ACM SF Bay Area Chapter — Data

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

采集时间：2026-10-19T22:16:35Z

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 these xbox products in
small_product_data.xml
.
We have also provided a sample benchmark submission and the code that produces it.
popular_skus.py
is a simple python script that predicts that each user clicked on one of the five most popular xbox skus. This script produces the benchmark in
popular_skus.csv
. Note that all of the predictions in popular_skus.csv are the same.
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.
Small 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 for more BigDataR tools
[https://github.com/koooee/BigDataR_Examples/tree/master/ACM_comp](https://github.com/koooee/BigDataR_Examples/tree/master/ACM_comp)