AI News, Building a Recommendation System Using Neural Network Embeddings
- On Saturday, October 6, 2018
- By Read More
Building a Recommendation System Using Neural Network Embeddings
To learn meaningful embeddings, our neural network must be trained to accomplish an objective.
Working from the guiding assumption of the project — that similar books link to similar Wikipedia pages — we can formulate the problem as follows: given a (book title, wikilink) pair, determine if the wikilink is present in the book’s article.
We give the network some true examples — actually present in the dataset — and some false examples, and eventually it learns embeddings to distinguish when a wikilink is on a book’s page.
To generate the false examples — done later — we’ll simply pick a link index and book index at random, make sure it’s not in the pairs, and then use it as a negative observation.
Note about Training / Testing Sets While using a separate validation and testing set is a must for a normal supervised machine learning task, in this case, our primary objective is not to make the most accurate model, but to generate embeddings.
- On Sunday, October 7, 2018
- By Read More
Neural Network Embeddings Explained
For example, if we have a vocabulary of 50,000 words used in a collection of movie reviews, we could learn 100-dimensional embeddings for each word using an embedding neural network trained to predict the sentimentality of the reviews.
The network I used has two parallel embedding layers that map the book and wikilink to separate 50-dimensional vectors and a dot product layer that combines the embeddings into a single number for a prediction.
In Keras code, this looks like the following (don’t worry if you don’t completely understand the code, just skip to the images): Although in a supervised machine learning task the goal is usually to train a model to make predictions on new data, in this embedding model, the predictions can be just a means to an end.
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