View Ratings of sweets (sweetrs) (public)

2011-09-13 15:28 by kidzik | Version 1 | Rating Empty StarEmpty StarEmpty StarEmpty StarEmpty StarEmpty Star
Rating
Empty StarEmpty StarEmpty StarEmpty StarEmpty StarEmpty Star Overall (based on 0 votes)
Empty StarEmpty StarEmpty StarEmpty StarEmpty StarEmpty Star Interesting
Empty StarEmpty StarEmpty StarEmpty StarEmpty StarEmpty Star Documentation
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Summary

Ratings of sweets for collaborative-filtering. Data gathered on http://sweetrs.org/ website.

DOI (more info at datacite)
10.5072/RATINGS-OF-SWEETS-SWEETRS
License
unknown (from UCI repository)
Dependencies
Tags
collaborative-filtering recommendation Regression sweetrs sweets
Attribute Types
Download
# Instances: 17903 / # Attributes: 3
HDF5 (220.0 KB) XML CSV ARFF LibSVM Matlab Octave

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Completeness of this item currently: 100%.
Original Data Format
h5
Name
sweetrs
Version mldata
0
Comment

CSV

Names
int0,int1,int2,
Data (first 10 data points)
    int0 int1 int2
    351 31 0
    57 9 3
    385 30 1
    286 23 4
    126 16 3
    371 46 0
    131 20 5
    116 17 3
    364 18 3
    180 40 2
    ... ... ...
Description

Format: user ID, product ID, rating rating is an integer in interval [1,5]

0 as rating indicates that user did not try given item yet.

For information about products check http://sweetrs.org/

Matrix is relatively dense - it has less than 7% unknown entities.

Rules:

  1. The user may not state or imply any endorsement from the sweetrs.org owners.

  2. The user must acknowledge the use of the data set in publications resulting from the use of the data set, and must an electronic or paper copy of those publications to admin@sweetrs.org.

  3. The user may not redistribute the data without separate permission.

  4. The user may not use this information for any commercial or revenue-bearing purposes without first obtaining permission from owners of sweetrs.org website.

URLs
http://sweetrs.org/
Publications
    Data Source
    http://sweetrs.org/
    Measurement Details

    User's provided ratings of the 1-5 scale or 0 if they haven't tried the product. Each user saw each item so in this sense the matrix is complete.

    Each user gave his ratings in one session.

    Usage Scenario

    Collaborative-filtering: 1. hiding some of known values 2. trying to predict them basing on others 3. check the error of prediction (RMSE, MSE, MAE or other).

    revision 1
    by kidzik on 2011-09-13 15:28

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    Acknowledgements

    This project is supported by PASCAL (Pattern Analysis, Statistical Modelling and Computational Learning)
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