/personalized-outfit-recommender-be

Primary LanguageJupyter Notebook

Next steps :

  1. outfit combination recommendation

  2. Recognizing Clothing Items Tagging and Categorization: Each clothing item needs to be tagged and categorized into types like tops, bottoms, shoes, accessories, etc. This is often done during the initial feature extraction and can be enhanced with a separate classification model if needed.

  3. Defining Outfit Rules Basic Rules: Define basic rules for outfit combinations, such as "a top with a bottom and shoes" or "a dress with shoes and an accessory". Advanced Rules: Create more sophisticated rules that take into account seasons, occasions, and fashion trends.

  4. Combination Generation Algorithm Rule-Based System: Implement a rule-based system that generates combinations based on predefined rules. AI-Based System: Use a machine learning model (e.g., decision trees, random forests) trained on fashion datasets to learn and generate outfit combinations.

Model Training Collaborative Filtering: Use algorithms like Matrix Factorization, SVD (Singular Value Decomposition), or neural collaborative filtering. Content-Based Filtering: Train a model using item features and user preferences. Hybrid Models: Combine collaborative and content-based filtering using models like LightFM.

Prediction and Recommendation Generating Outfit Combinations: Use the trained model to predict the compatibility of various item combinations. Scoring and Ranking: Score and rank the combinations based on predicted compatibility and user preferences.

Other Approaches Advanced Deep Learning Models: Use models like Variational Autoencoders (VAEs), Generative Adversarial Networks (GANs), or Transformer-based models for more sophisticated recommendations. Graph-Based Recommendations: Use graph-based algorithms like Graph Convolutional Networks (GCNs) to capture complex relationships between users and items.

  1. user profile and references
  2. enhanced ui/ux
  3. continuous learning

Model accuracy improvement :

  1. Data augmentation rotation, zooming, flipping, and color jitter to increase the diversity
  2. Fine tuning pretrained model Instead of just using the ResNet50 with a custom layer, fine-tune the last few layers of ResNet50.
  3. Transfer Learning with Advanced Models models like EfficientNet, InceptionV3, or MobileNetV2.
  4. Triplet Loss or Contrastive Loss Siamese network with triplet loss or contrastive loss to train the model.
  5. Embedding Quality Assessment visualization techniques like t-SNE or PCA

Evaluating Model Performance

  1. Metric Calculation: Use metrics like Precision, Recall, and F1-Score for retrieval tasks. Mean Average Precision (mAP) can also be used for evaluating the ranking of the retrieved items. For recommendation systems, use metrics like Precision@k, Recall@k, and MAP (Mean Average Precision).
  2. Validation Set: Create a validation set to regularly check the performance of the model during training. Use cross-validation to ensure the model's robustness.
  3. User Feedback: Implement a feedback system where users can rate the recommendations. Use this feedback to periodically update and improve the model.

Continuous Learning and Model Updates

  1. Periodic Model Retraining Set up a pipeline for periodic retraining of the model with new data. Use techniques like incremental learning if you don't want to retrain the model from scratch.
  2. User Interaction Data: Collect user interaction data (e.g., clicks, likes, purchases) to continually improve the model. Implement a system to periodically update the embeddings based on new user data.
  3. Feedback Loop: Create a feedback loop where user feedback is directly used to adjust the model. Use reinforcement learning techniques to adapt recommendations based on user feedback.