{ "fetched_at": "2026-10-19T07:42:51Z", "source_url": "https://www.kaggle.com/competitions/acm-sf-chapter-hackathon-big/data", "pages": [ { "name": "Prizes", "content": "

$500  1st place

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$350  2nd place

\r\n

$150  3rd place

", "isPublished": false, "hasIsPublished": false, "postTitle": "", "hasPostTitle": false, "mimeType": "", "hasMimeType": false }, { "name": "rules", "content": "\r\n\t\r\n

One account per participant

\r\n

You cannot sign up to Kaggle from multiple accounts and therefore you cannot submit from multiple accounts.

\r\n

No private sharing outside teams

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\r\n Privately sharing code or data outside of teams is not permitted.\r\n It's okay to share code if made available to all participants on the forums.\r\n

\r\n

Public dissemination of entries

\r\n

Kaggle and the competition host have the right to publicly disseminate any entries or models.

\r\n

Open licensing of winners

\r\n

\r\n Winning solutions need to be made available under\r\n a popular OSI-approved license\r\n in order to be eligible for recognition and prize money.\r\n

\r\n

Winning solutions must be posted or linked to in the forums.

\r\n

\r\n Prizes will be awarded after the winners have posted their solutions to the competition forum.\r\n Winners must post or link to their solutions within seven (7) days of being notified of their winning status.\r\n

\r\n

Team Mergers

\r\n

\r\n Team mergers are allowed and can be performed by the team leader. In order to merge, the combined team must have a total submission count less than or equal to the maximum allowed as of the merge date. The maximum allowed is the number of submissions per day multiplied by the number of days the competition has been running.
\r\n

\r\n\r\n

Team Limits

\r\n

There is no maximum team size.

\r\n

Submission Limits

\r\n

You may submit a maximum of 2 entries per day.

\r\n

You may select up to 5 final submissions for judging.

\r\n

Competition Timeline

\r\n

Start Date: 8/18/2012 4:09 PM UTC

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Merger Deadline: None

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Entry Deadline: None

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End Date: 9/30/2012 1:00 AM UTC

\r\n\t\r\n

[This section supercedes the GPL license stated earlier in the rules]

\r\n

Winning entries to the main competition must be open-sourced and licensed under Creative Commons Attribution-NonCommercial-ShareAlike 3.0 (\r\nCC BY-NC-SA 3.0).  This license allows work to be shared, modified, and used for Non-Commercial purposes with attribution to the author.

\r\n

Entries to the Visualization Prospect must be licensed under Creative Commons Attribution-NoDerivs 3.0 (\r\nCC BY-ND 3.0 ).  This license allows work to be shared (but not modified) and used for Commercial and Non-Commercial purposes with attribution to the author.

", "isPublished": false, "hasIsPublished": false, "postTitle": "", "hasPostTitle": false, "mimeType": "", "hasMimeType": false }, { "name": "data-description", "content": "

The main data for this competition is in the train.csv and \r\ntest.csv files. These files contain information on what items users clicked on after making a search.

\r\n

Each line of train.csv describes a user's click on a single item. It contains the following fields:

\r\n\r\n

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.

\r\n

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\r\n 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.

\r\n

In addition, there is information about products, product categories, and product reviews in\r\nproduct_data.tar.gz.

\r\n

We have also provided a sample benchmark submission and the code that produces it.\r\npopular_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\r\npopular_skus.csv.

\r\n

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\r\n order.

\r\n

Product Data Dictionary
\r\nhttps://bbyopen.com/documentation/products-api/product-attributes#TableProdRefInfo

\r\n

For more BestBuy Data and APIs, check out \r\nhttps://bbyopen.com/

\r\n

and more BigDataR tools https://github.com/koooee/BigDataR_Examples/tree/master/ACM_comp
\r\n

", "isPublished": false, "hasIsPublished": false, "postTitle": "", "hasPostTitle": false, "mimeType": "", "hasMimeType": false }, { "name": "Description", "content": "

3 months of real-world mobile behavior, 1.8 million clicks, 1.2 million users.

\r\n

\r\nYou can enter one or both tracks of the competition:

\r\n\r\n

There will also be a \r\nVisualization Contest that can be entered from either track.

\r\n
For more details on the event, go to:
\r\n

http://www.sfbayacm.org/DM-Hackathon-2012-10  (links to presentations were added 8/19/2012)

\r\n

Data Provided by:

\r\n

 \"\"

\r\n

Cloud Compute Sponsors:    

\r\n

     \"\"         \r\n\"\"     \r\n\"\"

", "isPublished": false, "hasIsPublished": false, "postTitle": "", "hasPostTitle": false, "mimeType": "", "hasMimeType": false }, { "name": "Recommended Reading", "content": "

\r\nYou may find useful material in the chapter 16 of the 

\r\n

\r\n2012 book “Scaling Up Machine Learning” or

\r\n

\r\nGraphLab SVD, Vowpal Wabbit, Mahout, MADlib

\r\n

\r\nhttp://en.wikipedia.org/wiki/Collaborative_filtering#Model-based

", "isPublished": false, "hasIsPublished": false, "postTitle": "", "hasPostTitle": false, "mimeType": "", "hasMimeType": false }, { "name": "Live Events", "content": "

If you are in the SF Bay Area...

\r\n

 

\r\n

\r\nKICKOFF MEETING:

\r\n

\r\nSaturday, August 18th,\r\n 9am 

\r\n

Lunch provided by Microsoft (Thank You)

\r\n

Cloud Center:  222 Caspian Dr, Sunnyvale, CA   www.SVcloudCenter.com 

\r\n

Free Registration for Kickoff (helps us estimate food):  http://sfbayacm.ticketleap.com/DM-Hackathon-2012-10

\r\n

\r\nTalks in the morning about the problem, HP and Microsoft Cloud infrastructure

\r\n

\r\nBigDataR Linux will provide a 2 hour course on Large Scale Learning in a Cloud Environment.
\r\n

\r\n

\r\n
\r\n

\r\n

\r\nAWARDS PRESENTED:

\r\n

\r\nAt the Data Mining Camp, Saturday, October 13 in San Jose

\r\n

\r\nWHAT is an UNCONFERENCE or CAMP?

\r\n

\r\nAn unconference is an event where users suggest topics, get together and discuss them in detail. This camp is focused on Data Mining, Analytics, Cloud Computing and the various applications of these technologies.

\r\n

\r\nhttp://www.SFbayACM.org/DMcamp2012

", "isPublished": false, "hasIsPublished": false, "postTitle": "", "hasPostTitle": false, "mimeType": "", "hasMimeType": false }, { "name": "Evaluation", "content": "

The evaluation metric for this competition is MAP@5.

\r\n

This metric is defined and explained \r\nhere.

", "isPublished": false, "hasIsPublished": false, "postTitle": "", "hasPostTitle": false, "mimeType": "", "hasMimeType": false } ] }