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Cosine_distance.py

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+import pandas as pd
+from gensim.models import Word2Vec
+import numpy as np
+from sklearn.metrics.pairwise import cosine_similarity
+from nltk.tokenize import word_tokenize
+from keras.preprocessing.text import Tokenizer
+from keras.preprocessing.sequence import pad_sequences
+
+def recommend_papers(positive_csv_path, unlabelled_csv_path, n):
+    # Load positive labelled data and unlabelled labelled data
+    positive_labelled_info = pd.read_csv(positive_csv_path)
+    unlabelled_labelled = pd.read_csv(unlabelled_csv_path)
+
+    # Combine title and abstract for positive labelled data and unlabelled labelled data
+    positive_labelled_info['text'] = positive_labelled_info['title'] + ' ' + positive_labelled_info['abstract']
+    unlabelled_labelled['text'] = unlabelled_labelled['title'] + ' ' + unlabelled_labelled['abstract']
+
+    # Preprocess text and tokenize
+    positive_labelled_info['tokenized_text'] = positive_labelled_info['text'].apply(word_tokenize)
+    unlabelled_labelled['tokenized_text'] = unlabelled_labelled['text'].apply(word_tokenize)
+
+    # Train Word2Vec model on the tokenized text of positive labelled data
+    word2vec_model = Word2Vec(sentences=positive_labelled_info['tokenized_text'], vector_size=100, window=5, min_count=1, workers=4)
+
+    # Initialize tokenizer
+    tokenizer = Tokenizer()
+    tokenizer.fit_on_texts(positive_labelled_info['text'])
+
+    # Convert text to sequences of integer indices
+    positive_sequences = tokenizer.texts_to_sequences(positive_labelled_info['text'])
+    unlabelled_sequences = tokenizer.texts_to_sequences(unlabelled_labelled['text'])
+
+    # Pad or truncate sequences to a fixed length
+    max_length = 50  # Adjust as needed
+    padded_positive_sequences = pad_sequences(positive_sequences, maxlen=max_length, padding='post', truncating='post')
+    padded_unlabelled_sequences = pad_sequences(unlabelled_sequences, maxlen=max_length, padding='post', truncating='post')
+
+    # Compute centroid of positive examples' word embeddings
+    centroid = np.mean([word2vec_model.wv[token] for token in tokenizer.index_word.values() if token in word2vec_model.wv], axis=0)
+
+    # Compute similarity scores for each document in the unlabelled labelled data
+    similarity_scores = []
+    for sequence in padded_unlabelled_sequences:
+        embeddings = [word2vec_model.wv[token] for token in sequence if token in word2vec_model.wv]
+        if len(embeddings) > 0:
+            similarity_score = cosine_similarity([centroid], [np.mean(embeddings, axis=0)])[0][0]
+        else:
+            similarity_score = 0  # Default similarity score for empty embeddings
+        similarity_scores.append(similarity_score)
+
+    # Add similarity scores to unlabelled labelled data
+    unlabelled_labelled['similarity_score'] = similarity_scores
+
+    # Sort unlabelled labelled data by similarity score in descending order
+    recommended_documents = unlabelled_labelled.sort_values(by='similarity_score', ascending=False)
+
+    # Return top n recommended documents
+    return recommended_documents[['id', 'title', 'similarity_score']].head(n)

requirements (1).txt

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+joblib
+scikit-learn
+gensim
+scipy
+nltk
+keras
+tensorflow