Video Recommendation Algorithm Required

@toriquldev123

Hello Dear! I write to introduce myself. I'm Engineer Toriqul Islam. I was born and grew up in Bangladesh. I speak and write in English like native people. I am a B.S.C. Engineer of Computer Science & Engineering. I completed my graduation from Rajshahi University of Engineering & Technology ( RUET). I love to work on Web Design & Development project. Web Design & development: I am a full-stack web developer with more than 10 years of experience. My design Approach is Always Modern and simple, which attracts people towards it. I have built websites for a wide variety of industries. I have worked with a lot of companies and built astonishing websites. All Clients have good reviews about me. Client Satisfaction is my first Priority. Technologies We Use: Custom Websites Development Using ======>Full Stack Development. 1. HTML5 2. CSS3 3. Bootstrap4 4. jQuery 5. JavaScript 6. Angular JS 7. React JS 8. Node JS 9. WordPress 10. PHP 11. Ruby on Rails 12. MYSQL 13. Laravel 14. .Net 15. CodeIgniter 16. React Native 17. SQL / MySQL 18. Mobile app development 19. Python 20. MongoDB What you'll get? • Fully Responsive Website on All Devices • Reusable Components • Quick response • Clean, tested and documented code • Completely met deadlines and requirements • Clear communication You are cordially welcome to discuss your project. Thank You! Best Regards, Toriqul Islam

@softsolution2000

I have extensive experience in Algorithm, Node.js, React.js, MongoDB, and Data Integration, making me a perfect fit for the Video Recommendation Algorithm project. I am confident in my ability to deliver high-quality code within your budget and timeline. Let's discuss the full project scope to adjust the budget accordingly. I am eager to start working on this project and showcase my skills. Please review my 15-year-old profile for past work. Looking forward to hearing from you.

@malkesh3m

⭐ Hello there, My availability is immediate. I read your project post on React/Node Developer for Video Recommendation Algorithm. I have extensive knowledge in MERN Stack (MongoDB/MySQL, Express JS, React, and NodeJS), as well as RESTful API integration. I am an experienced team of full-stack developers with skill sets in - React, JavaScript, jQuery, TypeScript, NextJS, React Native - NodeJS, ExpressJS - MongoDB, MySQL, PostgreSQL, SQLServer, SQLite - Redux, Thunk, GIT - JavaScript, HTML, CSS, Tailwind CSS - AWS, Azure, Digital Ocean, GoDaddy, Web Server Hosting Please send a message So we can quickly discuss your project and proceed further. I am looking forward to hearing from you. Thanks

@Tanmoy236

Hello! I'm an expert in recommendation systems and backend algorithm development with over 9 years of experience building production-ready content discovery engines for social platforms. I specialize in Node.js, MongoDB optimization, and hybrid recommendation approaches that balance personalization with content diversity. Here's how I can help: - Build a hybrid recommendation algorithm combining collaborative and content-based filtering with your existing MongoDB collections - Implement optimized aggregation pipelines delivering sub-500ms response times for personalized feeds - Create fully functional REST API endpoints integrated seamlessly with your React frontend - Deliver production-ready Docker configuration with complete documentation and unit tests My approach balances engagement signals, content similarity embeddings, and time-decay factors while avoiding filter bubbles through strategic serendipity injection. I'll ensure your algorithm handles cold-start scenarios and scales for concurrent user growth.

@zhens2

Having worked as a full-stack developer for the past decade, I have gained valuable experience in developing effective Recommendation Systems, working with emerging technologies like Node.js and React.js. My deep understanding of advanced querying systems in MongoDB enables me to optimize data retrieval and ensure seamless integration with the project's tech-stack. The solid foundation in machine learning engineering you seek in an ideal candidate is one of my specialties, making me highly proficient at implementing algorithms that blend collaborative filtering and content-based filtering just as you require -- factors that enhance recommendation accuracy while ensuring each user gets content they prefer. I believe my strong technical knowledge combined with a firm understanding of recommendation systems make me the best candidate to work on this video recommendation algorithm - guaranteeing not only optimal response times even under high user-loads but also accommodation of new users/videos (via cold-start handling) for usability. I offer more than just coding skills: I proactively analyze potential issues suh as scaling to support future growth or creating filter bubbles that we vow to keep away from our algorithms. With me on your team, you can be assured of quality performance, maximum security, and user satisfaction.

@DGM999

Hello, I’ve carefully reviewed your project and know exactly how to deliver results that are strategic, efficient, and tailored to your goals with no wasted time or guesswork, just real, measurable value. With several years of hands-on experience in Algorithm, Node.js, React.js, MongoDB, Data Integration, A/B Testing, API Development, REST API, I bring not just expertise but a track record of delivering results that help clients move forward with confidence. My Portfolio: https://www.freelancer.ca/u/DGM999 If you’re looking for reliable, quality-driven support, I’m just a message away. Let’s make it happen. Best Regards, Sadat Saeed

@techsty2014

Dear Client, I am excited about your project "Video Recommendation Algorithm Required" and confident I can deliver excellent results. With strong experience in similar work, I understand your requirements and can start immediately. I would love to discuss your project further and answer any questions. Thanks and best regards, Faizan

@AlexDeveloper911

Hi, We went through your project description and it seems like our team is a great fit for this job. We are an expert team which have many years of experience on Algorithm, Node.js, React.js, MongoDB, Data Integration, A/B Testing, API Development, REST API Please come over chat and discuss your requirement in a detailed way. Thank You

@thomasb726

⭐If you award me, your smile shows up.⭐ Hi Parth P. Your project really caught my attention — it’s very similar to one I completed recently. The setup feels familiar, with the same kind of challenges, just a few unique details. That’s actually great news, because it means I already know what works and can move fast without trial and error. Most importantly, I have deep experience and hands-on expertise with Algorithm, React.js, MongoDB, A/B Testing, Data Integration, REST API, Node.js and API Development. In my portfolio, you can find a project similar to yours. https://www.freelancer.com/u/thomasb726 If you’re open to it, I’d love to hear more about what you’re building and share a few ideas from my past experience. ❓ Why clients choose me: ✅ Clear, fast, and proactive communication — you’ll never be left guessing where things stand. ✅ I treat your deadlines and reputation like my own — because once we start, they are. ✅ Easy to talk to, quick to respond, and focused on making your job easier. ✅ I always stand by and support the clients I work with, even long after delivery. Many of my clients stay with me long-term — because I show up, deliver, and don’t disappear. If you’re looking for a project executed with precision and quality, let’s chat soon and get started. Best, Tom

@larasatik4

Hi Parth, I'm Larasati, an Algorithm Developer with hands-on experience building production-ready recommendation systems. Your 1-day delivery window is tight, but with a focused MVP we can ship a solid, scalable starter that integrates with your Node.js/Express backend and MongoDB data model. From your description, I would approach this by building a hybrid recommender: collaborative filtering for user-item patterns and content-based signals from video metadata and embeddings. We'll generate candidates from followed creators, similar content, and trending videos, then rank with a scoring function that weighs engagement (watch time, likes, shares, comments), metadata, completion rates, skip behavior, time decay, and content similarity. The pipeline will leverage your existing collections (mUsers, Videos, Interactions, Follows), with optimized MongoDB aggregations and embedding-driven similarity. Endpoints will be: GET /api/recommendations/:userId, POST /api/interactions/:videoId, GET /api/trending, POST /api/algorithm/retrain. We'll ship unit-tested modules, comprehensive docs, clear logging, and a Docker-ready setup for self-hosted deployment. Would you like me to prioritize fastest possible response time or deeper explainability and experimentation (A/B tests) for the initial deployment? Also, is multi-language support a requirement for the initial rollout, or planned for a later phase? Looking forward to discussing details. Best regards, Larasati

@robertw0802

Hi Parth, I’m Robert, a Senior Full-Stack & AI Engineer with over 10 years of experience architecting and delivering SaaS platforms, automation systems, and intelligent applications, specializing in recommendation algorithms, performance tuning, and scalable architectures. Recently, I successfully developed a video recommendation system for a social media platform using Node.js and MongoDB, combining collaborative filtering and content-based methods. My expertise aligns perfectly with your requirements, as I understand the intricacies of user engagement patterns and can implement a robust algorithm that integrates seamlessly with your existing tech stack. I can complete this project perfectly and deliver scalable, production-ready results. I am committed to clean architecture, structured documentation, and ensuring high performance with response times under 500ms, allowing for 1,000 simultaneous users. Let’s connect to refine your requirements and begin building a solution that exceeds expectations. What specific metrics do you want to prioritize for measuring recommendation success?

@homAfra

Hello, I’ve reviewed your requirements for a hybrid video recommendation system and can deliver a production-ready algorithm combining collaborative and content-based filtering, optimized MongoDB aggregations, and low-latency Node.js/Express APIs (<500ms). I’m a senior full-stack developer with 7+ years of experience, strong hands-on work in Node.js, Express, MongoDB, recommendation logic, engagement scoring, time-decay ranking, Dockerized deployments, and scalable backend architecture under tight deadlines. I’m available to start immediately and can complete the full implementation, APIs, documentation, and optional retraining endpoint within your 1-day timeline. Best regards, Homayun Afrabandpey

@devbaloni1983

Hello, I am a React Developer with 15+ years of experience in building modern, responsive, and high-performance web applications. I specialize in dynamic UIs, reusable components, API integration, and state management (Redux/Context API) to deliver seamless user experiences. Recently, I worked on OpenAI API integration for auto-generated articles, images, and social sharing, which shows my ability to adopt and implement the latest technologies. If you are looking for a dedicated React expert who ensures quality, innovation, and timely delivery, I’d be glad to work on your project.

@davig63

Hello Parth P., We went through your project description and it seems like our team is a great fit for this job. We are an expert team which have many years of experience on Algorithm, Node.js, React.js, MongoDB, Data Integration, A/B Testing, API Development, REST API Lets connect in chat so that We discuss further. Regards

@vitaliis27

⭐⭐⭐⭐⭐ I’ve built and deployed hybrid recommendation systems (collaborative + content-based) on Node.js/Express with MongoDB, optimized for low-latency feeds and high concurrency, and I can deliver a production-ready solution within your 1-day timeline. My approach uses multi-source candidate generation (follows, similarity embeddings, trending) with a time-decayed ranking model driven by engagement signals and efficient MongoDB aggregation pipelines to stay under 500 ms response times. I’ll provide clean, well-documented APIs, logging, basic unit tests, and optional Docker setup, with clear metrics on performance and recommendation quality. I’m available to start immediately and can share GitHub examples of recommendation and data-driven systems on request.

@SergMakh

Thanks for posting this project. I've checked it and it's really suitable for me - my skillset. I already have extensive experience in this field - tech what you need. My focus is not only the full functional product, but also the clean, modular code. I'd like to discuss the project details to confirm everything. Looking forward to your response. Sergey.

@mengxiang0215

I can deliver a production-ready hybrid recommendation algorithm within 24 hours using Node.js, Express, and MongoDB, combining collaborative filtering (user–user and user–creator signals) with content-based similarity via video embeddings and metadata scoring. I’ll implement efficient aggregation pipelines for candidate generation (follows, similar content, trending), apply time-decay and engagement-weighted ranking, expose all required REST endpoints, and document performance expectations, with clean, testable code that integrates directly into your existing collections and React frontend.

@patrycjaw6

I have hands-on experience building fast recommendation pipelines in Node.js with MongoDB and can implement a scalable hybrid recommender that balances personalization, recency, and diversity while avoiding filter bubbles. I’ll focus on optimized queries to keep response times under 500ms, include cold-start logic for new users and videos, deliver the required APIs, and provide clear documentation and retraining hooks so your team can iterate on ranking logic after launch.