AI Service Recommendation Engine - 20/03/2026 02:54 EDT

@MashoodurRehman1

I am an experienced ML/AI engineer with a strong background in building recommendation systems utilizing collaborative filtering, content-based methods, and hybrid approaches. My proficiency in Python and libraries like TensorFlow and PyTorch directly aligns with your project requirements. I have a solid track record of designing ETL pipelines for data cleaning and feature engineering, crucial for preparing user data for model training. In my previous projects, I have deployed recommendation engines with real-time callable APIs, ensuring seamless integration with main applications. I have worked extensively with evaluation metrics such as precision@k and recall to effectively measure model performance. Leveraging tools like AWS and MLOps platforms, I ensure scalable and maintainable solutions. My technical documentation skills will facilitate easy model adaptation as new data emerges. I am keen to discuss this project in detail, including model approach and timeline. Could you provide more specifics about the existing user data schema? I look forward to contributing to your AI service recommendation engine.

@divumanocha

Hi, As a top-tier software development team with over 12+ years of experience, and recognised experts in the field, we can provide you with an exceptional AI service recommendation engine that integrates seamlessly into your existing system. Our core technology stack is perfectly suited to your project requirements, including Python, TensorFlow, PyTorch and other similar libraries for your model development and maintenance needs. But more than just the tech-stack, what truly sets us apart is our comprehensive skillset in using AI/ML to build scalable, efficient solutions. We've successfully crafted various recommendation engines employing collaborative filtering, content-based approaches or advanced hybrid models - precisely what you need. Thanks....

@chiragardeshna

Hey, I carefully read your requirement for building an AI-based service recommendation engine. I understand you need a system that processes user data, builds recommendation models, and serves real-time suggestions. With 12+ years of experience in AI and machine learning, I have built recommendation systems using collaborative filtering, content-based, and hybrid approaches. I can design an ETL pipeline, perform feature engineering, and train models using Python with libraries like PyTorch or TensorFlow. I will also create a real-time API for recommendations and set up evaluation metrics like precision@k and recall. My focus is accuracy, scalability, and easy maintenance. Thanks Chirag

@utsav007pandya

Siap membangun engine rekomendasi AI yang meningkatkan relevansi layanan untuk setiap pengguna! Saya paham pentingnya akurasi dan skalabilitas agar rekomendasi selalu tepat sasaran. ✔️ Saya dapat memetakan skema data, membuat pipeline ETL untuk pembersihan & feature engineering, serta melatih model rekomendasi (collaborative, content-based, atau hybrid) sesuai kebutuhan. ✔️ Baru-baru ini, saya mengembangkan sistem rekomendasi untuk platform layanan digital, menghasilkan API real-time yang menyesuaikan rekomendasi dengan perilaku pengguna dan preferensi individual. ✔️ Saya juga menyiapkan metrik evaluasi seperti precision@k, recall, MAP, plus dokumentasi dan panduan pemantauan agar performa model tetap optimal seiring bertambahnya data. Portfolio / contoh proyek (placeholder – silakan diganti dengan link Anda): ✔️ Sistem rekomendasi berbasis Python/TensorFlow/PyTorch: [Link 1] ✔️ Pipeline ETL & feature engineering: [Link 2] ✔️ API real-time & integrasi aplikasi: [Link 3] Mari mulai proyek ini dan wujudkan rekomendasi yang personal, relevan, dan siap skala — saya bisa langsung menyiapkan rancangan model pertama!

@Solutionzhere

Hi, we’ve built AI recommendation engines (collaborative + hybrid) at SolutionzHere with real-time APIs and scalable pipelines. We’ll design ETL + feature engineering, train a hybrid model (CF + content-based), expose a REST API, and deliver evaluation (precision@k, recall) + monitoring guide. Rate: $25/hr (realistic for ML + backend work) Timeline: 2–3 weeks (MVP with API + metrics) We focus on accuracy + maintainability from day one. One key question: What user data do you currently have—interaction logs (clicks, views) or only profile/preferences?