Project Background:
Recommender systems are pivotal in todays digital landscape, helping users to discover relevant content, products, or services. This typically starts by highlighting the growing volume of data generated online and the increasing need for personalized recommendations, particularly crucial in e-commerce, content streaming, and social media platforms. Traditional recommender systems rely on collaborative filtering and matrix factorization based on diverse data types. Therefore, it emphasizes the rising interest in deep learning techniques that can handle large-scale, heterogeneous data and extract intricate user-item interactions. It may also reference the historical development of deep learning in recommendation systems, underscoring how these methods have evolved to address the unique challenges posed by the recommendation domain.
Problem Statement
- The recommender systems revolve around the limitations of traditional recommendation approaches and the need for more effective, personalized, and scalable recommendation systems.
- While conventional methods such as collaborative filtering and matrix factorization have been successful to some extent often struggle with the complexities of modern recommendation tasks.
- It includes sparse data, the cold-start problem for new users and items, and the difficulty of capturing intricate user-item interactions and preferences in heterogeneous datasets.
- Deep learning techniques offer a promising solution to these issues, as they can automatically learn complex patterns from vast and diverse data sources, enabling more accurate and context-aware recommendations.
- The problem statement entails the development of innovative deep-learning models that can effectively address and provide personalized and context-aware recommendations while ensuring computational efficiency and user transparency.
Aim and Objectives
- This project aims to leverage advanced deep learning techniques to revolutionize recommendation systems.
- The primary objective is to provide users with highly personalized and context-aware recommendations across various domains, enhancing user experiences and engagement.
- Develop deep learning-based recommender systems significantly to improve recommendation accuracy by capturing complex user-item interactions more relevant.
- Create models that can provide personalized recommendations taking into account individual user preferences, behavior, and context, such as location, time, and device, ensuring a tailored user experience.
- Devise strategies and algorithms that can effectively address data sparsity issues common in recommendation systems, allowing for recommendations even when data on user preferences is limited.
- Design models and systems that can scale efficiently to handle large datasets and accommodate growing user bases, ensuring that recommendations remain effective as systems expand.
- Develop techniques to tackle the problem involving recommendations for new users and items with minimal historical data by leveraging content-based recommendations and hybrid models.
Contributions to Recommender Systems using Deep Learning
1. The foremost contribution lies in significantly improving recommendation accuracy and personalization. By employing deep neural networks, these systems excel in capturing intricate user-item interactions and patterns from vast datasets.
2. This results in recommendations that closely align with individual preferences, elevating user satisfaction and engagement on platforms ranging from e-commerce to content streaming.
3. Another key contribution is effectively mitigating data sparsity issues inherent in traditional collaborative filtering-based recommender systems.
4. Deep learning recommenders address these challenges by combining content-based information with collaborative filtering techniques and utilizing auxiliary data sources.
5. Incorporating online learning techniques marks yet another contribution that has ushered in real-time and adaptive recommendations.
6. These systems can swiftly adapt to changing user preferences and trends, ensuring that recommendations remain pertinent in dynamic scenarios. This dynamic adaptability enhances user engagement and utility.
Deep Learning Algorithms for Recommender Systems
- Restricted Boltzmann Machines (RBMs)
- Deep Factorization Machines (DeepFM)
- Convolutional Neural Networks (CNNs)
- Recurrent Neural Networks (RNNs)
- Long Short-Term Memory (LSTM)
- Gated Recurrent Unit (GRU)
- Matrix Factorization
- Neural Collaborative Filtering (NCF)
- Autoencoders
- Multi-Layer Perceptrons (MLPs)
- Variational Autoencoders (VAEs)
- Session-based Recurrent Models
- Attention Mechanisms
- Transformer-based Models
- Knowledge Graph Embedding Models
Datasets for Recommender Systems using Deep Learning
- MovieLens
- Netflix Prize Dataset
- Last.fm Dataset
- Spotify Playlist Data
- Jester Online Joke Recommender Data
- Million Songs Dataset
- CrossValidated Q&A Data
- TaFeng Online Retail Dataset
- Pinterest Content Recommendation Challenge Dataset
- Music Technology Group (MTG) Dataset
- Book-Crossing Dataset
- Rendles MovieLens Data
- Steam Video Game Recommendations Data
Performance Metrics
- Mean Absolute Error (MAE)
- Root Mean Squared Error (RMSE)
- Mean Squared Logarithmic Error (MSLE)
- Precision
- Recall
- F1-Score
- Normalized Discounted Cumulative Gain (NDCG)
- Area Under the Receiver Operating Characteristic Curve (AUC-ROC)
- Area Under the Precision-Recall Curve (AUC-PR)
- Hit Rate
- Coverage
- Novelty
- Mean Reciprocal Rank (MRR)
- Entropy-based Metrics
- Rank-based Metrics
Software Tools and Technologies
Operating System: Ubuntu 18.04 LTS 64bit / Windows 10
Development Tools: Anaconda3, Spyder 5.0, Jupyter Notebook
Language Version: Python 3.9
Python Libraries:
1. Python ML Libraries:
- Scikit-Learn
- Numpy
- Pandas
- Matplotlib
- Seaborn
- Docker
- MLflow
2. Deep Learning Frameworks:
- Keras
- TensorFlow
- PyTorch
