cdr_example.py

# 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.
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#
#     http://www.apache.org/licenses/LICENSE-2.0
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"""Example for Collaborative Deep Ranking (CDR)"""

import cornac
from cornac.data import Reader
from cornac.datasets import citeulike
from cornac.eval_methods import RatioSplit
from cornac.data import TextModality
from cornac.data.text import BaseTokenizer


# CDR composes an autoencoder with a ranking collaborative model to represent item texts and user-item interactions
# The necessary data can be loaded as follows
docs, item_ids = citeulike.load_text()
feedback = citeulike.load_feedback(reader=Reader(item_set=item_ids))

# Instantiate a TextModality, it makes it convenient to work with text auxiliary information
# For more details, please refer to the tutorial on how to work with auxiliary data
item_text_modality = TextModality(
    corpus=docs,
    ids=item_ids,
    tokenizer=BaseTokenizer(stop_words="english"),
    max_vocab=8000,
    max_doc_freq=0.5,
)

# Define an evaluation method to split feedback into train and test sets
ratio_split = RatioSplit(
    data=feedback,
    test_size=0.2,
    exclude_unknowns=True,
    item_text=item_text_modality,
    verbose=True,
    seed=123,
    rating_threshold=0.5,
)

# Instantiate CDR model
cdr = cornac.models.CDR(
    k=50,
    autoencoder_structure=[200],
    max_iter=100,
    batch_size=128,
    lambda_u=0.01,
    lambda_v=0.1,
    lambda_w=0.0001,
    lambda_n=5,
    learning_rate=0.001,
    vocab_size=8000,
    seed=123
)

# Use Recall@300 for evaluation
rec_300 = cornac.metrics.Recall(k=300)

# Put everything together into an experiment and run it
cornac.Experiment(eval_method=ratio_split, models=[cdr], metrics=[rec_300]).run()