TF-recomm

Shoparn is an AI e-commerce tool that allows furniture shoppers to visualize products in their rooms. It does not require 3D models; users upload a photo to see realistic results. Shoparn integrates with Shopify and WooCommerce. It helps furniture stores reduce returns and increase conversions.

Backdrops - Wallpapers is a premier mobile application designed to provide users with an extensive and exclusive collection of high-quality wallpapers. The platform distinguishes itself by offering hundreds of original designs meticulously crafted in-house by the Backdrops team, ensuring unique backgrounds not found elsewhere. Beyond its curated selection, Backdrops fosters a vibrant community where users can contribute their own original artwork and photography, subject to a rigorous approval process to maintain quality standards. This collaborative approach enriches the library with diverse and high-caliber submissions. A standout feature is the "Wall of the Day," which daily showcases new creations or team favorites, providing a fresh visual experience for returning users. The app caters to a wide array of aesthetic preferences, offering various styles such as material, minimal, and abstract designs. Backdrops aims to be the definitive source for device personalization, combining exclusive content with community engagement and daily inspiration to keep screens looking fresh and unique.

SparrowRecSys is a deep learning movie recommendation system. It is a Maven-based project that includes TensorFlow, Spark, and Jetty Server modules. The system is designed for learning and improving recommendation systems.

HLLM enhances sequential recommendations using hierarchical large language models for item and user modeling. It also includes HLLM-Creator for personalized creative generation. The tool is designed to improve the accuracy and personalization of recommendation systems. HLLM leverages large language models to understand user preferences and item characteristics.

Recommenders is a GitHub repository providing best practices for building recommendation systems. It offers examples, algorithms, and evaluation tools for collaborative filtering and content-based filtering. The repository is open-source and designed for developers, data scientists, and machine learning engineers. It provides resources for building and evaluating recommendation models.

DeepMatch is a deep matching model library for recommendations and advertising. It allows users to train models and export representation vectors for ANN search. The library is designed to be easy to use for building recommendation systems and implementing advertising solutions.