Ranking Amazon Reviews by Quality with Pointwise Ratings learned from Pairwise Data

How do you rank reviews without a helpfulness score? Learn a novel technique that turns pairwise comparisons into a continuous quality metric using TensorFlow and BERT.

#1about 7 minutes

Ranking Amazon reviews by learning a quality score

The model learns to assign a continuous quality score to reviews by training on a classification task rather than a direct regression problem.

#2about 7 minutes

How neural networks learn through feature embeddings

Neural networks transform complex input data into a high-dimensional embedding space where it becomes linearly separable for classification.

#3about 6 minutes

Processing sequential text data with Word2Vec and RNNs

Word2Vec creates semantic vector representations of words, which are then processed sequentially by Recurrent Neural Networks (RNNs) to understand context.

#4about 5 minutes

Identifying the limitations of recurrent neural networks

Recurrent neural networks are slow due to their sequential nature, lack general-purpose understanding, and struggle to process bidirectional context simultaneously.

#5about 8 minutes

Introducing the BERT and transformer architecture

The Transformer architecture uses self-attention to process text in parallel, enabling BERT to learn general, contextual word representations through masked language modeling.

#6about 6 minutes

Demonstrating self-attention with a character-level model

A simplified character-level model called 'Artist BERT' demonstrates how self-attention works by predicting masked characters in artist names.

#7about 8 minutes

Training a pointwise ranker from pairwise review data

A Siamese network architecture processes pairs of reviews to determine which is better, implicitly learning a pointwise quality score for each individual review.