Content-Based Recommendation System Using Word Embeddings

In his article Dhilip Subramanian describes a book recommendation engine based on goodreads data. He explains that “a content-based recommendation system recommends books to a user by taking similarity of books based on the description. It also considers the user’s previous book history in order to recommend a similar book. Cosine similarity is used in our recommender system to recommend the books.”

The repository is at https://github.com/sdhilip200/Content-Based-Recommendation—Good-Reads-data.

The code is given in a Python notebook. When I ran the code, I faced a few errors and had to change the code, as follows:

Cell [2] : df = pd.read_csv("test.csv")
I replaced test.csv with data.csv
df = pd.read_csv("data.csv")

Cell [9] : google_model.build_vocab(line_of_sentance)
I replaced line_of_sentance with corpus
google_model.build_vocab(corpus)

Cell [9] : %%time
SyntaxError: invalid syntax
I commented it.

Cell [13] : cosine_similarities = cosine_similarity(array_embeddings, array_embeddings)
I replaced array_embeddings with word_embeddings
cosine_similarities = cosine_similarity(word_embeddings, word_embeddings)

I forked the repository and made the changes.
It can be accessed at: https://github.com/mh-github/Content-Based-Recommendation—Good-Reads-data