Source code for cornac.datasets.citeulike
# Copyright 2018 The Cornac Authors. All Rights Reserved.
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# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
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# distributed under the License is distributed on an "AS IS" BASIS,
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# ============================================================================
"""
This dataset is mostly from the paper 'Collaborative topic modeling for recommending scientific articles'
[Wang and Blei - KDD 2011]. It was further collected, named `citeulike-a`, and used in the paper
'Collaborative Topic Regression with Social Regularization' [Wang, Chen and Li - IJCAI 2013].
Link to the data: http://www.wanghao.in/CDL.htm
"""
from typing import List
from ..utils import cache
from ..data import Reader
[docs]
def load_feedback(reader: Reader = None) -> List:
"""Load the implicit feedback between users and items
Parameters
----------
reader: `obj:cornac.data.Reader`, default: None
Reader object used to read the data.
Returns
-------
data: array-like
Data in the form of a list of tuples (user, item, 1).
"""
fpath = cache(url='https://static.preferred.ai/cornac/datasets/citeulike/users.zip',
relative_path='citeulike/users.dat', unzip=True)
reader = Reader() if reader is None else reader
return reader.read(fpath, fmt='UI', sep=' ', id_inline=True)
[docs]
def load_text():
"""Load item texts including tile and abstract joined together into one document per item.
Returns
-------
texts: List
List of text documents, one per item.
ids: List
List of item ids aligned with indices in `texts`.
"""
import csv
texts, ids = [], []
fpath = cache(url='https://static.preferred.ai/cornac/datasets/citeulike/text.zip',
relative_path='citeulike/raw-data.csv', unzip=True)
with open(fpath, 'r', encoding='utf-8', errors='ignore') as f:
next(f)
for row in csv.reader(f, delimiter=',', quotechar='"'):
ids.append(row[0])
texts.append(row[3] + '. ' + row[4])
return texts, ids