Machine learning is used in almost every part of the system at major search engines like Google, Bing. However, most e-commerce websites are powered by search engines which provide excellent ROI and help in retaining and finally converting the user for a sale. In this hack session, come and witness how to use NLP based Deep Learning models to effectively design search platforms with a focus on e-commerce use case.
Section 1: Query Understanding
Description and Jupyter-notebook demonstration:
NLP based Deep Learning Models for finding the intent of a Query in a particular taxonomy/categories:
- Multi-Label/Multi-Class Classification Model from scratch in Keras,
- Feature Engineering in Spark Scala and pandas, Keras Functional APIs details in TF 2.0,
- ImageNet moment of NLP – Latest invention in Word embeddings – ELMO and BERT
NLP based Deep Learning Models for Query Tagging with entities like Brand, Color, Nutrition, product quantity, etc. using Named Entity Recognition:
- Sequence modeling using Convolutional Random Fields (CRF),
- Building a custom model in Tensorflow Estimator API,
- Traditional Word Embeddings like Glove, Fasttext, etc.
Section 2: Related and Refined Searches
Description and Jupyter-notebook demonstration:
- Related Searches: Building Sequence to Sequence (Seq2Seq) model using Long Short Term
Memory (LSTM) concept of Deep Neural Network for predicting next search keywords,
Sequential APIs details in Keras - Refined Searches: Similarity Search based on FAISS(Facebook AI Research) for giving a similar refined search keyword,
comparing different word Embeddings e.g. word2vec, Fasttext, glove, etc. in popular AI framework such as gensim.
Section 3: ML Model Deployments in Production
- Serving the model in production using Tensorflow and Keras
Key Takeaways:
- Learn about Natural Language Processing techniques like Word-Embeddings – BERT / ELMo, Bi-LSTM Networks.
- Application of NER and seq2seq modeling in the search domain.
- Get to know about various Multi-Class / Multi-Label Classification Problems related to Search Domains
