Recsys - Embeddings
- Overview
- Factorization Machines vs. Matrix Factorization
- Representing Demographic Data
- Content-Based Filtering
- Comparative Analysis
Overview
- This article will go over different methods of generating embeddings in recommender systems.
- Embeddings are a key component in many recommender systems. They provide low-dimensional vector representations of users and items that capture latent characteristics. Here are some common embedding techniques used in recommenders:
Neural Collaborative Filtering (NCF)
- Input: User-item interaction data (e.g. ratings, clicks)
- Computation: Trains a neural network model on the interaction data to learn embeddings for users and items that can predict interactions. Combines matrix factorization and multi-layer perceptron approaches.
- Output: Learned user and item embeddings.
- Advantages: Captures complex non-linear patterns. Performs well on sparse data.
- Limitations: Requires large amounts of training data. Computationally expensive.
Matrix Factorization (MF)
- Input: User-item interaction matrix.
- Computation: Decomposes the matrix into low-rank user and item embedding matrices using SVD or ALS.
- Output: User and item embeddings.
- Advantages: Simple and interpretable.
- Limitations: Limited capability for sparse and complex data.
Factorization Machines (FM)
- Input: User features, item features, interactions.
- Computation: Models feature interactions through factorized interaction matrix. Captures linear and non-linear relationships.
- Output: User and item embeddings.
- Advantages: Handles sparse and high-dimensional data well. Flexible modeling of feature interactions.
- Limitations: Less capable for highly complex data.
Graph Neural Networks (GNNs)
- Input: User-item interaction graph.
- Computation: Propagate embeddings on graph using neighbor aggregation, graph convolutions etc.
- Output: User and item node embeddings.
- Advantages: Captures graph relations and structure.
- Limitations: Requires graph data structure. Computationally intensive.
Factorization Machines vs. Matrix Factorization
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Key differences:
- Modeling approach: MF directly factorizes interaction matrix. FM models feature interactions.
- Handling features: MF doesn’t explicitly model features. FM factorizes feature interactions.
- Data representation: MF uses interaction matrix. FM uses feature vectors.
- Flexibility: MF has limited modeling capability. FM captures non-linear relationships.
- Applications: MF for collaborative filtering. FM for various tasks involving features.
Representing Demographic Data
Approaches for generating user embeddings from demographics:
- One-hot encoding: Simple but causes sparsity.
- Embedding layers: Maps attributes to lower dimensions, capturing non-linear relationships.
- Pretrained embeddings: Leverage semantic relationships from large corpora.
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Autoencoders: Learn compressed representations via neural networks.
- Choose based on data characteristics and availability of training data.
Content-Based Filtering
- Represents items via content features like text, attributes, metadata.
- Computes user-item similarities using TF-IDF, word embeddings, etc.
- Recommends items similar to user profile.
Comparative Analysis
The choice of embedding technique depends on the characteristics and requirements of the recommender system:
- Use NCF or DMF for systems involving complex non-linear relationships and abundant training data.
- Prefer MF when interpretability is critical and data is limited.
- FM excels for sparse data with rich features.
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GNNs are suitable for graph-structured interaction data.
- Here’s a table summarizing the different methods and their characteristics to help you decide which approach to choose for your recommendation system:
Method | Use Case | Input | Output | Computation | Advantages | Limitations |
---|---|---|---|---|---|---|
Neural Collaborative Filtering (NCF) | Collaborative filtering with deep learning | User-item interaction data | User and item embeddings | Training neural networks | Captures complex patterns in data | Requires large amounts of training data |
Matrix Factorization (MF) | Traditional collaborative filtering | User-item interaction matrix | User and item embeddings | Matrix factorization techniques | Simplicity and interpretability | Struggles with handling sparse data |
Factorization Machines (FM) | General-purpose recommender system | User and item features, interaction data | User and item embeddings | Factorization of feature interactions | Handles high-dimensional and sparse data | Limited modeling capability for complex data |
Deep Matrix Factorization (DMF) | Matrix factorization with deep learning | User and item features, interaction data | User and item embeddings | Deep neural networks with factorization | Captures non-linear interactions | Requires more computational resources |
Graph Neural Networks (GNN) | Graph-based recommender systems | User-item interaction graph | User and item embeddings | Graph propagation algorithms | Captures relational dependencies in data | Requires graph-based data and computation |