Open Issues Need Help
View All on GitHubAI Summary: Create a CONTRIBUTING.md file outlining contribution guidelines and establish GitHub issue templates for bug reports, feature requests, and evaluation feedback to improve the project's accessibility and credibility for external contributors.
A modular, explainable recommendation pipeline leveraging multiple strategies—collaborative filtering, embeddings, and fallback logic—for robust, personalized product recommendations in real-world scenarios.
AI Summary: Rename the project's LICENSE.txt file to LICENSE to ensure GitHub correctly identifies and displays the project's MIT license.
A modular, explainable recommendation pipeline leveraging multiple strategies—collaborative filtering, embeddings, and fallback logic—for robust, personalized product recommendations in real-world scenarios.
AI Summary: Develop an interactive user interface (UI) using Streamlit or Gradio to demonstrate the existing multi-strategy recommendation system. The UI should allow users to input a user ID and receive a list of recommended products, along with the strategies used and optionally, a user profile summary. This will facilitate quick demonstrations and stakeholder presentations.
A modular, explainable recommendation pipeline leveraging multiple strategies—collaborative filtering, embeddings, and fallback logic—for robust, personalized product recommendations in real-world scenarios.
AI Summary: The task requires modifying the existing recommendation pipeline to include a 'strategy_used' field in the output JSON for each recommended item. This field should indicate which recommendation strategy (e.g., Node2Vec, Fallback) was used to generate the recommendation. For fallback strategies, an additional 'reason' field explaining why the fallback was used (e.g., 'cold_start') should also be included. This enhances the transparency and explainability of the recommendation system.
A modular, explainable recommendation pipeline leveraging multiple strategies—collaborative filtering, embeddings, and fallback logic—for robust, personalized product recommendations in real-world scenarios.
AI Summary: The task involves evaluating LightFM or Implicit ALS as alternative or supplementary recommendation models to the existing Surprise SVD model within a multi-strategy recommendation pipeline. This includes data preprocessing to create the necessary interaction matrix, training the chosen model (LightFM or Implicit ALS), and comparing its performance (Precision@5, Recall@5, and coverage) against the current SVD-based personalized strategy. The goal is to benchmark the scalability and personalization capabilities of these alternative approaches.
A modular, explainable recommendation pipeline leveraging multiple strategies—collaborative filtering, embeddings, and fallback logic—for robust, personalized product recommendations in real-world scenarios.
AI Summary: Implement a configuration file (YAML or JSON) to control the activation of different recommendation strategies (SVD, Node2Vec, Fallback) within the existing recommendation pipeline. The pipeline's main logic needs to be modified to read and interpret this configuration file to dynamically assemble the active strategies for each user.
A modular, explainable recommendation pipeline leveraging multiple strategies—collaborative filtering, embeddings, and fallback logic—for robust, personalized product recommendations in real-world scenarios.