How to Build a Winning Deep Learning Powered Recommender System Part 3

Building a Winning Deep Learning Recommender System: A Step-by-Step Guide

Summary

Building a winning deep learning recommender system requires a thorough understanding of recommender system concepts, deep learning models, and effective data preprocessing and feature engineering techniques. This article provides a comprehensive guide on how to build a deep learning-powered recommender system, focusing on the NVIDIA team’s first-place solution for the Booking.com challenge.

Introduction

Recommender systems are crucial for businesses to personalize user experiences and increase engagement. Traditional methods like collaborative filtering and content-based filtering have limitations, such as the cold-start problem and filter bubbles. Deep learning models offer a more comprehensive approach by leveraging user-item interactions and contextual information.

Deep Learning for Recommender Systems

Deep learning models for recommender systems can be broadly categorized into two phases: training and inference. During training, the model learns to predict user-item interaction probabilities by presenting it with examples of past interactions. In the inference stage, the model is deployed to infer the likelihood of new interactions.

Key Components of a Deep Learning Recommender System

1. Candidate Generation: Pair a user with hundreds or thousands of candidate items based on learned user-item similarity.
2. Candidate Ranking: Rank the likelihood that the user enjoys each item.
3. Filtering: Show the user the item they are rated most likely to enjoy.

Deep Neural Network Models for Recommendation

Deep learning recommender models build upon existing techniques such as factorization and embeddings to handle categorical variables. Embeddings are learned vectors representing entity features so that similar entities have similar distances in the vector space.

DLRM: A Deep Learning-Based Model for Recommendations

DLRM is a model introduced by Facebook Research that uses both categorical and numerical inputs. It maps categorical data to dense representations using embedding layers and computes second-order interactions between features.

Session-Based Recommendations

Session-based recommendations apply sequence modeling from deep learning and NLP to recommendations. RNN models train on sequences of user events to predict the probability of a user clicking on a candidate item.

NVIDIA Merlin: An Open-Source Framework for Deep Learning Recommender Systems

NVIDIA Merlin is an open-source application framework built on NVIDIA RAPIDS, CUDA Deep Neural Network library (cuDNN), and Triton. It facilitates and accelerates recommender systems on GPU, speeding up common ETL tasks, training of models, and inference serving.

Building a Winning Deep Learning Recommender System

The NVIDIA team’s first-place solution for the Booking.com challenge focused on predicting the last city destination for a traveler’s trip given their previous booking history within the trip. The solution involved exploratory data analysis, feature preprocessing and extraction, model training, and validation.

Feature Preprocessing and Selection

Feature engineering and selection are iterative processes that start with engineering new features, training a model, and evaluating the model predictions against target labels. The goal is to determine which features improve the model’s prediction accuracy.

Best Practices for Building and Deploying Recommender Systems

1. Data Preprocessing: Aggregate, extract, and clean raw data sources to create relevant features.
2. Feature Engineering: Use tools like NVIDIA NVTabular and RAPIDS to accelerate preprocessing on GPUs.
3. Model Training: Train models using deep learning frameworks like NVIDIA Merlin.
4. Model Validation: Evaluate model predictions against target labels to determine accuracy.

Table: Key Components of a Deep Learning Recommender System

Table: Deep Learning Models for Recommender Systems

Table: Best Practices for Building and Deploying Recommender Systems

Conclusion

Building a winning deep learning recommender system requires a comprehensive understanding of recommender system concepts, deep learning models, and effective data preprocessing and feature engineering techniques. By following the steps outlined in this guide and leveraging tools like NVIDIA Merlin, businesses can create personalized user experiences that drive engagement and revenue.