Video Content Recommendation Engine

@sajjadtaghvaeifr

Hello, I trust you're doing well. I am well experienced in machine learning algorithms, with nearly a decade of hands-on practice. My expertise lies in developing various artificial intelligence algorithms, including the one you require, using Matlab, Python, and similar tools. I hold a doctorate from Tohoku University and have a number of publications in the same subject. My portfolio, which showcases my past work, is available for your review. Your project piqued my interest, and I would be delighted to be part of it. Let's connect to discuss in detail. Warm regards. please check my portfolio link: https://www.freelancer.com/u/sajjadtaghvaeifr

@vishal1145

Your recommendation engine will fail if you treat this as a static model problem. The real challenge is cold-start for new videos, concept drift as user preferences shift weekly, and serving latency under 100ms when you scale past 10K concurrent users. I've built three production recommender systems that increased engagement by 30-50%, and the difference between a demo and a revenue-driving system is how you handle these edge cases. Before I architect the solution, I need clarity on two things: What's your current video catalog size and daily upload velocity? If you're adding 500 videos per day, we need a streaming pipeline that retrains embeddings hourly. Second, what's your acceptable cold-start period? If a new user needs personalized recommendations within their first three interactions, we'll need a hybrid model that falls back to content-based filtering before collaborative signals kick in. Here's the architectural approach: - PYTHON + PYTORCH: Build a two-tower neural network that learns user and video embeddings in the same latent space, enabling sub-50ms inference via approximate nearest neighbor search with FAISS. - FEATURE ENGINEERING: Extract temporal signals (watch time decay, session recency), content features (video duration, category embeddings), and interaction patterns (skip rate, rewatch behavior) to capture intent beyond raw clicks. - API DEVELOPMENT: Deploy a FastAPI service with Redis caching for top-N recommendations and a fallback layer that serves trending content when user history is sparse, ensuring zero cold-start failures. - STREAMLIT DASHBOARD: Build an internal evaluation UI where you can A/B test model versions, visualize precision-recall curves, and monitor real-time metrics like CTR lift and average watch time per session. - MLOPS PIPELINE: Implement automated retraining triggers when model drift exceeds 5% on holdout metrics, using Airflow to orchestrate daily batch jobs and streaming updates for high-velocity content. I've deployed similar systems for a video platform that scaled from 50K to 2M users without re-architecting the inference layer. Let's schedule a 20-minute technical call to walk through your data schema and define success metrics before I draft the full implementation roadmap.

@ashinnd84

Hello there, we are a team of developers and we can do this project in no time. Thanks Ashish Kumar.

@iimtpradeepdash

Hi! Your focus on measurable lift in watch-time and click-through is spot on. Most video platforms aim for novelty, but real value comes from tracking engagement at each step. You’re right to look beyond just collaborative filtering; two-tower models make sense given the mix of metadata and user logs. I’ve delivered similar recommendation engines for SaaS platforms where real-time ranking was key, plus full API output for downstream use. For yours, I’d map out a flexible data schema—video tags, user event logs, and time-decayed interaction scores. Initial model: matrix factorization for fast proof, then a two-tower deep network once more data rolls in. One question: do you plan to refresh model weights daily, or is near-real-time retraining in scope? It impacts how I’d design the pipeline. Happy to send a 1-page sketch showing the feature plan and milestones, free. You can also see more demos of my work at work.techindika.com. — Pradeep

@Web1devloper

Leveraging my 8+ years in Data Science and a host of relevant skills, I can deliver precisely what your project needs: an end-to-end, powerful recommendation engine. My experience working across multiple domains including finance, healthcare, e-commerce, and SaaS will enable me to apply the most apt classical approaches or cutting-edge deep-learning architectures to your dataset for maximum click-throughs and watch-time. My experience with recommender systems, predictive analytics, and data storytelling has equipped me with the ability to translate complex datasets into user-friendly, actionable insights. Upon kickoff, I'll provide you with a milestone-driven timeline, starting with a feasible proof of concept before rapidly moving into production deployment. To benchmark success, I propose utilizing established accuracy metrics alongside meticulous A/B testing plans to ensure we tailor the algorithm's output to perfection. In terms of tools and languages, Python will take center stage for its compatibility with TensorFlow/PyTorch which are ideally aligned for ML implementation. Additionally, I am well versed in SQL for efficient data querying and visualization tools such as Power BI and Tableau to create insightful dashboards that summarize the wealth of information generated from the developed system.

@dushyant317

Hello, I can build your end-to-end video recommendation engine with real-time ranking, behavioral analytics, and scalable recommendation APIs using Python, FastAPI, PyTorch/TensorFlow, and vector-search architecture. I have experience designing AI pipelines for personalized recommendation systems including candidate retrieval, deep ranking models, and low-latency inference optimized for CTR and watch-time improvement.

@southa5

Hello. I am a PhD student working in the area of computational science, deep learning, optimization, and algorithm design. If you need a reliable and long term collaboration, I am the best person to work with.

@NEHABHAT92

Having been an industry professional for over nine years, my competency in Python is extensive and well-rounded for the task at hand. Previously, I've been involved in both web and mobile app developments, which provided me relevant expertise in manipulating datasets and managing API development. My approach to your job will encompass a delicate blend of classical methods such as collaborative filtering, as well as modern deep-learning architectures employing TensorFlow or PyTorch. Understanding your core need for real-time video recommendations, I am committed to delivering an end-to-end solution-based system that ensures seamless data ingestion, model training, and ultimately a highly-efficient micro-service that returns ranked video suggestions. Throughout my career, I have developed peculiar insights into data mining and efficient algorithms which should serve to optimize accuracy metrics including CTR and watch-time—carving a better online experience for your users. Finally, what distinguishes my proposal from the rest is not just my extensive experience but also the overall value I strive to bring. From concise timelines to optimized project costs, you won't find a more competent yet cost-effective candidate for this endeavor. Let’s not leave any room for doubts - with me navigating your project - your progress will be real!

@jdbsoftech

Hello Dear, I am Jagmohan Kumawat, a multidisciplinary digital expert with 5+ years of experience building luxury websites, mobile apps, digital marketing, automations, intelligent systems and high impact creative assets. My focus is always on delivering results with precision, style and longterm scalability. Signature Excellence • Luxury Websites & Web Systems • iOS & Android Mobile App Development • Custom API Integrations • Advanced Workflow Automation & AI Systems • Salesforce CRM Setup, Flows & Cloud Integrations • Professional Branding & UI/UX • Social Media Content & Digital Strategy • E-Commerce Systems & Automation • Premium Video Creation & Cinematic Editing Technology & Creative Mastery Node.js | React | Laravel | WordPress | PHP | JS | Shopify | Android/iOS | Salesforce Cloud | CRM Automations | REST APIs | Firebase | AI Solutions | Figma | Adobe Suite (Illustrator, Photoshop, Premiere Pro, After Effects) Canva Pro | AutoCAD | Motion Graphics & AI Video | Digital Marketing | Content Writer | SEO/SMO I’m ready to take your project forward with precision, creativity and technical excellence. Feel free to share more details. I will prepare a customize plan for your exact needs. Best Regards Jagmohan Kumawat

@rudrab76

I can develop a scalable video recommendation engine using Python and ML techniques with API integration, real-time recommendations, model training, and optimized ranking for better user engagement and watch time.

@Mutharasi128

Hello, I’m interested in building your end-to-end video recommendation system with real-time personalized ranking and scalable deployment. Proposed Approach: • Content-based filtering using metadata, tags, and NLP embeddings • Collaborative filtering for behavioral learning • Two-tower retrieval + sequence-aware ranking for better personalization and watch-time Key Signals: • Watch history and session behavior • CTR, watch percentage, likes, skips • Video metadata, recency, popularity, creator info • Demographic and temporal patterns Tech Stack: Python, TensorFlow/PyTorch, Scikit-learn, FastAPI, Redis, PostgreSQL/MongoDB, FAISS/ScaNN, Spark (if needed). Milestones: 1. Data ingestion & feature engineering 2. Baseline recommender model 3. Advanced ranking/retrieval models 4. Real-time API deployment 5. Evaluation & optimization Success Metrics: • Precision@K • Recall@K • NDCG • CTR lift • Watch-time improvement • A/B testing I have experience with AI/ML systems involving recommendation logic, behavioral analytics, and scalable APIs. I can deliver an initial working model within a few weeks and optimize iteratively based on engagement data. Looking forward to collaborating.

@markdennis19

Hi, Recommender systems are a core part of my ML work — here's my proposed approach: Model Stack: - Phase 1 (PoC): Collaborative filtering + content-based hybrid using video metadata and interaction logs - Phase 2: Two-tower deep model (user tower + video tower) for scalable real-time retrieval - Phase 3: Sequence-aware re-ranking (transformer or GRU-based) for session context Additional signals worth adding: watch-completion rate, re-watch events, time-of-day context — these measurably lift CTR and watch-time. Feature Plan: user embeddings (history, demographics), video embeddings (tags, category, duration, engagement stats), interaction signals (clicks, skips, completions). Stack: Python, PyTorch, FastAPI (serving), FAISS (vector search), MLflow (experiment tracking). Milestones: 1. Week 1–2: Data pipeline + baseline model 2. Week 3–4: Two-tower model + API endpoint 3. Week 5: Evaluation (Precision@K, NDCG, watch-time lift) + A/B test plan Success benchmarks defined at kickoff against your baseline CTR. What does your current data volume look like (users / videos / interactions)? Best regards

@Kartik13000

Hi there, Your project aligns perfectly with my core expertise. I build end-to-end ML systems and can deliver a working first-cut model within your timeline using a scalable, modern architecture. For a fast-to-deploy yet highly accurate system, I recommend starting with a Two-Tower Neural Network built with PyTorch or TensorFlow Recommenders. This cleanly separates the video features (metadata, tags) from user features (demographics, sequence logs) to compute high-speed, real-time embeddings. My Recommended Project Proposal: Model Stack: Two-Tower architecture for retrieval, followed by a light gradient-boosted ranking layer (LightGBM/XGBoost) to optimize directly for watch-time and CTR. I will use FastAPI for the inference microservice and Streamlit to build a quick, interactive dashboard for you to test sample user recommendations in real time. I am ready to look at your sample data and get a robust proof-of-concept running within the first two weeks. Let’s jump on a quick chat to sync on the dataset format and kick this off!

@futuremaster777

Real-time video ranking depends on a seamless pipeline connecting metadata ingestion, model training, and a low-latency microservice API. This architecture ensures that user-specific suggestions are served instantly by optimizing the data flow between the feature store and the inference engine. Focusing on a content-based approach allows for high-precision recommendations based on video attributes and user interaction history. The system will be built to handle scalable data ingestion while maintaining the responsiveness required for a smooth user experience. Working estimate for the full scope: full delivery INR 45000, 60 days. Milestone structure: 25% kickoff milestone / 75% completion milestone. Final scope, timeline, and budget can be adjusted during discussion.

@ApieTech

Hey — read the brief. Framing the win on CTR + watch-time lift (not just accuracy) is the right call — keeps the model honest. Approach: two-tower model (user tower + content tower) trained on interaction logs, FAISS for candidate retrieval over content embeddings, lightweight re-ranker on top. Two-tower beats pure collab filtering when metadata is rich and scales — vector lookup stays sub-ms at 100K+ catalog. Stack: Python + PyTorch for training, FAISS for the retrieval index, FastAPI for the serving micro-service, Redis for online feature caching, Streamlit for the internal eval dashboard. Milestones (6 weeks): 1. Data audit + matrix-factorization baseline — wk 1-2 2. Two-tower model, offline metrics (Recall@K, NDCG) — wk 3-4 3. FastAPI serving + Redis online features — wk 5 4. A/B harness + CTR/watch-time tracking — wk 6 Bid ₹35,000 covers POC through serving API. One sizing question: - Target scale at launch — users, catalog size, peak QPS for recommendations? Ping me, can share past recsys work and walk through the model choice. — Rohan, APIE TECH

@reimeiyuki

Hi — your goal is measurable lift in CTR and watch-time, so that's what I'll optimize for, and I'll prove it on your own data before you commit. Approach: a hybrid baseline first — content-based (embeddings over video metadata/tags) + collaborative filtering (implicit-feedback ALS). Explainable, fast to a PoC, solves cold-start. Add a two-tower model only if it earns its lift. Data/features: videos (metadata, tags, embeddings), interactions (user, video, watch-time, timestamp), users (demographics). Signals: content embeddings, watch-time-weighted implicit feedback, recency/popularity, session sequence. Serving: a FastAPI microservice returning ranked suggestions in real time (FAISS) + a Streamlit dashboard for live metrics. Low-risk for you: Milestone 1 = a working baseline on YOUR data with real offline metrics (Recall@K, NDCG). You see the lift before going further. Scope of this bid (Rs 25,000): baseline + API + dashboard + offline benchmarking, delivered Dockerized (docker compose up) with source + README — you host it, no live session. Two-tower, online A/B and scaling are follow-on milestones quoted later. Stack: Python, implicit/scikit, PyTorch, FAISS, FastAPI, Streamlit. Question: for v1, roughly how many users, videos and daily requests? Share sample data and I'll start the PoC. — Yuki

@nourhank72

Hello, I have carefully reviewed your requirements and understand that you need a complete video recommendation system that handles data ingestion, model training, ranking, and real-time recommendation delivery through an API. I specialize in Machine Learning, Recommendation Systems, and Python-based AI pipelines using TensorFlow/PyTorch, FastAPI, and modern ML workflows. What I will deliver: * End-to-end recommendation pipeline * Feature engineering from user behavior and video metadata * Recommendation models (content-based, collaborative filtering, or hybrid) * Real-time recommendation API * Evaluation and benchmarking system * Clean, modular, and reproducible codebase * Documentation and deployment instructions Approach: * Analyze interaction and metadata signals * Build ranking and recommendation workflows * Compare multiple recommendation approaches * Optimize for CTR, watch-time, and recommendation quality * Ensure scalable architecture and maintainable code Technologies: Python, TensorFlow/PyTorch, Scikit-learn, FastAPI, Pandas Why I am a strong fit: * Strong experience in ML and recommendation systems * Experience building scalable AI pipelines and APIs * Focus on performance, reproducibility, and clean architecture I am ready to start immediately. Best regards, Nourhan Khaled

@akshatt69

I can build a scalable content-based video recommendation engine that delivers personalized video suggestions in real time using a hybrid recommendation approach. The system will combine content-based filtering, collaborative filtering, and deep learning models such as Two-Tower Retrieval Networks and sequence-aware recommendation architectures to maximize CTR, watch-time, and user retention. The project will include: • End-to-end data pipeline for ingesting video metadata, user interaction logs, demographic tags, and behavioral signals • Feature engineering using watch history, session duration, engagement patterns, search queries, device type, and temporal/contextual signals • Model training and evaluation pipeline using TensorFlow/PyTorch with support for scalable experimentation • Real-time recommendation API/microservice for ranked video retrieval with low latency • Candidate generation + ranking architecture for production-scale recommendations • Benchmarking framework using Precision@K, Recall@K, NDCG, CTR uplift, watch-time improvement, and A/B testing strategy Suggested stack: Python, TensorFlow/PyTorch, FastAPI, Spark, PostgreSQL/BigQuery, Redis, Docker, Kubernetes, and MLflow for experiment tracking. Development plan: 1. Data analysis & feature planning 2. Proof-of-concept recommendation model 3. Hybrid/deep-learning optimization 4. API & deployment pipeline 5. Real-time serving + evaluation dashboard

@khyatim23

Hi! Your project caught my attention immediately because I've already built a recommendation engine in production — a CNN (ResNet50) + KNN reverse image search system serving personalized results across a 10,000+ item catalogue, deployed on Streamlit with real users. My proposed approach for your video engine: Phase 1 (PoC — Week 1-2): Content-based filtering using video metadata + NLP embeddings on tags/descriptions. Fast baseline to validate data quality. Phase 2 (Week 3-4): Two-tower model (user tower + video tower) in PyTorch for scalable retrieval. Combined with KNN/FAISS for sub-50ms real-time lookup. Phase 3 (Week 5): FastAPI microservice + Redis caching. Streamlit dashboard for live A/B monitoring of CTR and watch-time lift. Key signals I'd add beyond what you listed: watch-completion rate, rewatch events, skip patterns, and time-of-day context — these measurably lift watch-time in production. Success benchmarks: Precision@K, NDCG, CTR lift, and watch-time improvement — defined against your baseline at kickoff. Stack: Python, PyTorch, FAISS, FastAPI, AWS S3 (for model artifacts), Streamlit. I'm GATE DA 2026 qualified with hands-on ML deployment experience. I'd love to review your sample data and get a PoC running in the first two weeks. One quick question: what's your approximate video catalogue size and daily active users? This will shape the retrieval architecture.