Building Scalable Retrieval System with Two-Tower Models | Query-Item Retrieval Recsys Model | ML AI
Channel: DataTrek
Date: 2023-11-17

#datascience #machinelearning #artificialintelligence #recsys #recommendations #amazon 

AmazonProductSearch MiniDataset + Input Embeddings (Dataset + Sentence (Raw) Embeddings Creation https://www.kaggle.com/code/abhishekmungoli/amazonproductsearch-minidataset-input-embeddings 
Two-Tower Search-Item Retrieval Recommendation Model Training https://www.kaggle.com/code/abhishekmungoli/two-tower-retrieval-recommendation-model-training 
Meituan's Game-Changing Recsys Model | KDD'21 | Two-Tower Retrieval Model | Reddit Ads Platform | AI https://youtu.be/UhpbTSbi3lI
Lessons from Instagram's Cutting-Edge Explore Recommendation System | Retrieval to Reranking | Meta https://www.youtube.com/watch?v=Myna6rnmCG8
Model-Based Account Recommendations @ Twitter | Recommendation System | Retrieval & Ranking | RECSYS https://www.youtube.com/watch?v=Xqo8fwgjxW4
Master the Art of Model Compression with Knowledge Distillation | Future of Model Deployment https://youtu.be/1N_EBJUOjVU
Approximate Nearest Neighbour and a popular Library Annoy for k-Nearest Neighbour Search https://youtu.be/DSQOrBTqmYA
Approximate Nearest Neighbor and Product Quantizer for k-Nearest Neighbor | Embeddings Search https://youtu.be/50PNumB7s3U
Train your own Product Embeddings | Embeddings across Domains | Training on 1.02 Billion rows on GPU https://youtu.be/DN4S96oHRhE

Explore the power of Two-Tower Model Architecture in this YouTube video! Discover how this deep learning approach is revolutionizing retrieval tasks for search and recommendation systems. Whether one is in e-commerce, content search, or social networking, Two-Tower Models offer incredible flexibility and scalability.

In this video, we will take a deep dive into the realm of Two-Tower Models, which comprise two distinct towers—one for queries and one for items. These models have the potential to revolutionize various recommendation and search tasks, whether it's finding relevance between users and products, queries and documents, people and people, or users and images/videos, among others. The versatility of the two-tower architecture extends to any pair of entities we wish to analyze.

We will also demonstrate the process of training these neural networks using Amazon's open-source datasets to address query-item retrieval challenges, making them highly adaptable to the specific requirements. Once trained, these models generate embeddings for the entities involved, facilitating the efficient retrieval of relevant items at scale.

In the context of e-commerce, we will discuss the limitations of relying solely on word matching and why it's imperative to take into account other factors and actual purchase behavior to enhance the retrieval of more relevant items. We will explore the concept of fine-tuning embeddings by incorporating additional signals such as category, price, ratings, reviews, and personalized data. This approach significantly boosts the scalability and accuracy of your retrieval system.

In this video, we will go through the process of building a Two-Tower Search-Item Retrieval model from scratch using an open-source Amazon dataset in details, allowing one to witness firsthand how this architecture can create a highly effective and scalable retrieval system. The codes and all the relevant links are provided.

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