Recommender Systems Using Reinforcement Learning

Introduction

Recommender systems play a pivotal role in today's digital landscape, helping users discover products, movies, music, and more. These systems have evolved significantly over the years, from traditional content-based and collaborative filtering methods to cutting-edge techniques like reinforcement learning (RL). In this article, we'll explore the different approaches to recommendation systems and then delve into a state-of-the-art RL-based recommender system.

Traditional Approaches

Before we venture into the world of RL, let's briefly discuss two traditional recommendation methods:

1. Content-Based Filtering: This approach recommends items based on their features and attributes. For instance, in a movie recommendation system, it might suggest films with similar genres or actors.
2. Collaborative Filtering: Collaborative filtering relies on user-item interactions. It identifies users with similar preferences and recommends items that those like-minded users have enjoyed.

The Power of Reinforcement Learning

Reinforcement Learning (RL) takes recommendation systems to a whole new level. Instead of static recommendations, RL algorithms make sequential decisions, optimizing over time based on user interactions.

The State-of-the-Art: Deep Reinforcement Learning (Deep RL)

Among RL techniques, Deep Reinforcement Learning (Deep RL) stands out as a powerful tool for recommendation systems. It offers the ability to make personalized recommendations by learning from user behavior and feedback.

Deep Q-Network (DQN): DQN is a model-free RL algorithm that estimates expected rewards for different actions. It's a natural fit for recommendation systems, where actions involve suggesting products or items to users.

Deep REINFORCE: Deep REINFORCE is another Deep RL algorithm. Unlike DQN, it focuses on stochastic policies, outputting probability distributions over recommended items. This approach has gained traction due to its ability to capture uncertainty and diversity in recommendations.

The Amazon Fashion Use Case

The Data

Our dataset comes from the renowned researcher Julian McAuley, specifically the Amazon Fashion Review dataset. This dataset is a valuable resource for fashion product reviews on Amazon.

Data Preprocessing

Before diving into the algorithmic details, let's discuss the essential data preprocessing steps:

1. Data Loading and Cleaning: We load the dataset and filter out unverified reviews and those with missing overall ratings.
2. NLP for Text Analysis: We use spaCy for NLP-related tasks, such as noun extraction and stopword removal. This step helps us extract valuable information from review text.
3. Grouping by Reviewers: We group reviews by reviewers to understand their preferences and purchasing behavior. This insight will be crucial for making personalized recommendations.

The Algorithms

Our approach leverages Reinforcement Learning (RL) algorithms, specifically two techniques: Deep Q-Network (DQN) and Deep REINFORCE. These algorithms will enable us to make sequential decisions and recommend products based on customer interactions.

1. Deep Q-Network (DQN): DQN is a model-free RL algorithm that learns to make decisions by estimating the expected rewards of different actions. It's well-suited for recommendation systems, where actions involve suggesting products to users.
2. Deep REINFORCE: Deep REINFORCE uses policy convergence over time to make recommendations. Unlike DQN, it focuses on stochastic policies and outputs a probability distribution over recommended products.

The Results

Our goal is to predict recommended products for users based on their historical interactions. We define rewards based on whether our recommendations align with users' future purchases. The higher the reward, the better our recommendations.

In our experiments, we observed that Deep REINFORCE performed exceptionally well compared to DQN. It exhibited faster convergence and improved recommendation accuracy. This aligns with our problem statement, as it creates a stochastic policy that outputs a probability distribution over recommended products.

Conclusion

We introduced our journey into building a recommendation system for Amazon Fashion products. We discussed the data preprocessing steps and the choice of RL algorithms.

The whole code is available at our medium article.

Please do not forget to follow my startup Immortal Brains.

Citations

Justifying recommendations using distantly-labeled reviews and fine-grained aspects Jianmo Ni, Jiacheng Li, Julian McAuley Empirical Methods in Natural Language Processing (EMNLP), 2019

Read the paper