1. Spacy-model for word embedding

• Convert article content into word vectors
• https://github.com/explosion/spacy-models/releases//tag/en_core_web_lg-2.2.5
• Installation

    pip install spacy
    python -m spacy download en_core_web_lg

2. Embedding Neural Network for content features:

• tensorflow
• keras

3. Input Data (cleaned data, no NaNs and deduplicated):

• Article contents: articleId (string), title (string), category (string)
• User behavior: UserId (string), articleId (string)

Five arguements:

• two input filenames: TEST_ARTICLES.csv (articleId, title, category) and TEST_CLICKS.csv (userId, articleId)
• NUM_ARTICLES: number of articles selected from
• TOP_NUM_ARTICLES: select NUM_ARTICLEs from the top popular articles
• NUM_EVAL_ARTICLES: number of articles for evaluation
• K: top-K recommendations
• SEED: random seed

  python Comparison_results_companydata.py TEST_ARTICLES.csv TEST_CLICKS.csv NUM_ARTICLES TOP_NUM_ARTICLES NUM_EVAL_ARTICLES K SEED