Netflix-Style Platform & Recommender - 30/07/2026 07:38 EDT

@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

@koolvishruth

Hi Sir, I am from banglore.I have 7 years of experience in Machine learning Engineering.I will deliver as mentioned everything in Git repository with step-by-step setup instructions and a short read-me explaining model choice, hyper-parameters, and any libraries used (Python, TensorFlow, Surprise, etc.).Let's connect

@mahads31

Having developed numerous web and mobile applications, I believe my skills, especially in API development and Python programming, make me the perfect fit for your project. I have a deep understanding of building complex systems and have previously worked on projects similar to yours, leveraging data to develop powerful recommender engines. Your demand for timeliness and performance poses no problem for me; I have experience optimizing code to handle heavy data flows and would ensure that your requirement for a P95 response time under 150 ms is not just met but exceeded. For scalability reasons, I would propose using a model-based approach for your collaborative-filtering engine, having previously attained praiseworthy outcomes with this method. Moreover, not only am I accustomed to working with Git repositories and writing explicit read-me notes, I also possess exemplary documentation skills that would be employed fully to provide clear instructions in line with your demand. My proficiency in technologies such as Python and TensorFlow among others enhances my ability to drive efficient results for your project. Choosing me guarantees robust systems built with a long-term view in mind; seeing technology as a tool for lasting business success as you do.

@vishal1145

Your cold-start constraint is the real bottleneck here. Most collaborative-filtering implementations fail when users have fewer than five ratings because the similarity matrix becomes too sparse to generate meaningful neighbors. If you do not solve this with a hybrid fallback or content-based bootstrapping layer, your first-time users will see generic trending lists instead of personalized picks. Quick questions - are you planning to scale beyond 100k users in the next 12 months, and do you have any metadata on the movies themselves (genres, cast, runtime) that we can use to augment the collaborative signal during cold starts? Here is the architectural approach: - COLLABORATIVE FILTERING: I'll implement a matrix-factorization model using Surprise or PyTorch that decomposes the ratings matrix into latent factors, then cache user embeddings in Redis to hit your 150ms P95 target without recomputing on every request. - API DEVELOPMENT: FastAPI endpoints with async workers will serve precomputed recommendations from cache, falling back to a lightweight content-based ranker for cold-start users to maintain the 0.4 Shannon entropy threshold. - STREAMING PLATFORM: I'll build the catalog browser and playback shell in React with a Node backend that logs implicit signals (watch time, skips) so you can retrain the model weekly and improve accuracy over time. I've built two production recommender systems that scaled from 50k to 500k users without latency degradation. Let's schedule a 20-minute technical call to walk through the cold-start strategy and caching architecture before you commit to a build.

@ashinnd84

Hello sir/madam, we are a team of senior AI ML Full Stack Web and Mobile App Developers. Please, send me a message to discuss the work and finish in no time. Thanks Ashish Kumar.

@axiomswb

Your ratings-and-reviews-only constraint is the hardest part of this build, and it is where I am starting. SVD++ from the Surprise library beats memory-based KNN here because neighbourhood lookups degrade sharply past a few thousand titles — at your 10k catalogue size the latency penalty alone would blow the 150ms P95 ceiling before the recommendation logic even runs. SVD++ pre-computes latent factors once and serves from memory. For cold-start users with fewer than five ratings, I will blend SVD++ output with a genre-entropy fallback that samples across categories until Shannon entropy clears 0.4 — this is not a patch, it is a first-class code path with its own test coverage. Latency layer: user embeddings pre-computed on a background scheduler, results cached in Redis, so your "Top Picks" and "Because You Watched" FastAPI endpoints hit cache rather than the model on every call. One problem you have not mentioned: SVD++ retraining frequency. With a live ratings stream, a stale model drifts fast. I will build an incremental update hook so new ratings feed the model without a full retrain cycle. Delivery: Git repo, README covering hyperparameter choices, step-by-step setup. 22,500 INR, 7 days. Are your existing ratings explicit numerical scores, or do the reviews carry watch-time or sentiment signals I should fold into the training matrix?

@ammarahmed20

Could a model-based collaborative-filtering engine help achieve the dynamic recommendations you’re looking for? I can definitely implement this while ensuring it utilizes the user ratings effectively. With over 5 years of experience in Python and machine learning, I have built and deployed recommendation systems that prioritize responsiveness and accuracy. I fully understand the importance of both the P95 response time and maintaining diverse recommendations, even for cold-start users. I’ll create clean API endpoints to deliver those “Top Picks” and “Because You Watched…” suggestions with sub-150 ms latency. Additionally, I’ll implement core streaming features to provide a realistic testing environment for the recommender. All code will be managed in a Git repository alongside detailed setup instructions, model explanations, and hyper-parameters used. I can share relevant demos of my past work if needed. Let’s get started on building your streaming platform. Best Regards,

@iimtpradeepdash

The scaling question is memory-based CF can work for 100k users/10k titles if latent vectors are stored smart, but for growth and sub-150 ms latency you’ll want a model-based approach (SVD or light hybrid). I’ve shipped a SaaS product using a recommendation layer trained on explicit ratings for route-matching (PackSmart AI, NDA). Metrics and approach available on call. I’ll build the recommendation service in Python using Surprise, train/test your user/items matrix, and expose fast Node.js endpoints for “Top Picks” and “Because You Watched…” calls. The minimal streaming front (React.js + sample assets with HTML5 video) will integrate via these APIs. Cold-start diversity is handled by fallback genre-based recommendations and entropy checks. Ready to show a skeleton repo and quick infra sketch if you want details on hyper-parameter selection or latency. Pradeep

@Hrolgar

At 10k titles and 100k users, a full user-item matrix is under a gig sparse, so item-based collaborative filtering with implicit ALS is the right backbone here, not a heavier model-based approach that fights cold start and latency both. The real risk in your acceptance criteria is entropy on Top Picks for users with zero history, and P95 under 150ms once candidates get re-ranked per request. Plan: ALS for warm users, a content-vector fallback (genre, cast, description) for cold start, blended so a new profile gets a diverse first screen instead of trending-now on repeat. Recommendations precompute and cache per user, with a light online re-rank at request time to hit 150ms without recomputing the model per call. Because You Watched reuses the same item-similarity index Top Picks builds, so it's one pipeline, not two. Catalog, playback, and profile scaffolding get built just deep enough to demo the recommender end to end. M1: data model, ingestion, ALS baseline wired to Top Picks. 14850 INR, 4 days. M2: cold start blending, Because You Watched, and the re-ranking layer for the latency target. 15150 INR, 4 days. M3: catalog/playback/profile scaffolding plus a load test at 10k titles and 100k users. 15000 INR, 2 days. 45000 is built off what's in the brief, the cold start weighting and how many genre tags you're tracking will move it once I see real catalog metadata. Send the repo or sample dataset and I'll have a tighter number back tomorrow.

@iwebphpdeveloper

Hello, I can build your full-stack movie streaming platform with a fast, personalized recommendation engine based on collaborative filtering. The platform will include user profiles, movie browsing, playback, and recommendation sections like Top Picks and Because You Watched, all backed by clean, scalable APIs. The project will be well-structured, performance-optimized, and delivered with complete source code, setup instructions, and clear documentation for easy deployment and future updates. I'm ready to get started and discuss the best approach for your project. Best Regards, Abhijeet

@hoangvanp

Hello, Creating a seamless movie streaming site with a robust recommendation engine is about enhancing user experience through personalized suggestions. The key will be developing a collaborative-filtering engine tailored to your structured user ratings and reviews dataset. To ensure scalability, we can discuss the advantages of both memory- and model-based approaches. I propose focusing on building efficient API endpoints for real-time movie suggestions like "Top Picks" and "Because You Watched...". Additionally, implementing core streaming features will provide a realistic environment to showcase the recommender's capabilities. In past projects, I've successfully deployed similar recommendation engines, optimizing for diverse recommendations and low response times. I look forward to discussing the specifics of your project and sharing relevant experience. A few questions: - Have you considered any specific technologies for the implementation? - Are there any particular genres or user behaviors that should be prioritized in the recommendations? Best regards,

@sonika1419

**DO NOT PAY ME UNTIL I COMPLETE! :)** Hello my valuable client :) My profile is new over here but I have 7 years of experience in this field. I have completely understood about your project. Also I will provide you free maintenance on your project for 1 year after project completion. I can definitely complete this in your timeframe. Give me one chance to prove myself. Hit the chat button to get started. If you will not like my work then you dont need to pay me any money so dont worry and have faith in me :) I am eagerly waiting for your message.

@srdank001

Hi. I have experience building full stack streaming platforms and recommendation systems using Python, FastAPI, PostgreSQL, Redis, and collaborative filtering. I would use a model based approach for better scale, with cached real time recommendations and a popularity fallback for cold start users. I can deliver Top Picks, Because You Watched, playback, profiles, and catalog browsing while targeting sub 150 ms API latency. I’d be glad to review your dataset and share the best model design for 100k users and 10k titles. Thanks Srdan.

@alluankit01

Hi, I can build your Netflix-style movie streaming demo with a collaborative-filtering recommendation engine, clean API endpoints, catalog browsing, playback flow, and basic user profiles. The best solution is to use a hybrid recommender approach: model-based collaborative filtering for scalability, plus fallback logic for cold-start users using ratings, popularity, and diversity rules. This will allow “Top Picks” and “Because You Watched” rows to work reliably even when users have limited history. I’m comfortable with Python, recommender systems, collaborative filtering, Surprise/LightFM/scikit-learn, FastAPI, API development, data preprocessing, ratings/review datasets, latency optimization, and full-stack demo integration. Deliverables include: * Collaborative-filtering engine * Cold-start recommendation logic * Top Picks API * Because You Watched API * Basic user profiles * Catalog browsing * Playback demo flow * Diversity-aware recommendation output * Performance testing notes * Git repository source code * Setup instructions * README with model choice and parameters I’ll focus on a fast, explainable, and realistic recommender-backed streaming platform demo that meets your latency, diversity, and setup requirements. Best regards Ankit

@rohank2502

I can start today - a collaborative-filtering recommender plus a clean Netflix-style front-end is squarely my lane. How I would build it: - Recommender: I would start with model-based collaborative filtering (matrix factorisation / SVD via the surprise or implicit libraries) over your ratings/reviews - it scales better than pure memory-based as the catalogue grows. I will benchmark both and show you the accuracy tradeoff so the choice is evidence-based, not a guess. - Serve personalised suggestions through a clean REST API, wired into a responsive Netflix-style browse UI (React) with rows, search, and detail pages. - Clean documented code, plus a short note on how the model retrains as new ratings arrive. - I work with modern AI-assisted tooling, so I move fast and you will see a working recommender early. One question to scope it: roughly how many users/movies/ratings are in the dataset, and is video playback in scope for v1 or is the focus the recommender + browse experience? Ready to begin right away.

@JoseRCarvas

Hi - the recommender is the real product here, so I'll lead with that. I'll build a collaborative-filtering engine on your ratings/reviews dataset and, importantly, evaluate it properly: a train/test split with metrics (RMSE for rating prediction, plus Precision@K / Recall@K and coverage for the ranked suggestions) so we can see it actually works on your data, not just runs. I'll compare a memory-based approach (item-item / user-user) against a model-based one (matrix factorisation such as SVD/ALS) and recommend which fits your scale, with cold-start handled via popularity/attribute fallbacks. The engine exposes a clean API (recommendations for a user, similar titles for an item), and I'll wrap it in a lean, responsive streaming-style front end to demo browsing + personalised rows so it feels like the product. Stack: Python (pandas + scikit-surprise/implicit) + a simple web layer. You get source, the evaluation notebook, and docs. Full catalogue/streaming at scale is a natural phase 2. ~10 days. - Jose

@NEHABHAT92

I am Neha, a seasoned web and mobile app developer with a vast repository of experience exceeding 9+ years. Fluent in multiple languages such as Python, Java and PHP among others, I possess a comprehensive arsenal of skills required for your project. Having built E-commerce sites and Content Management Systems, I have dealt with data at a massive scale before and can offer unique expertise to enhance your site's recommendation engine. In regards to the recommender model specifically, I have deep knowledge in collaborative-filtering within Python, including libraries like TensorFlow and Surprise. This extensive experience enables me to make an informed decision about the most suitable approach for your project - whether memory-based or model-based collaborative-filtering depending on the scale. What sets my services apart is my commitment to offering holistic packages. You won't just get clean APIs and a strong backend for flawless performance, but also SEO optimization, compatibility across browsers and devices, sleek UI/UX design along with prompt post-delivery support for three months all at an efficient cost. With me, your project will be in capable hands throughout its cycle from inception to execution. With a track-record of turning IDEAS TO REALTY proficiently ,I ensure every detail from initial setup to final step is carefully documented so as to enable easy comprehension for future reference or updating by other developers. I & Recommender project

@InjectAI

Hello, I can build your **full-stack movie streaming platform** with a high-performance **collaborative filtering recommendation engine** at its core. I have experience with **Python, Django/FastAPI, Machine Learning, recommendation systems, REST APIs, and full-stack web development**. I recommend a **model-based collaborative filtering** approach (e.g., Surprise/SVD or TensorFlow embeddings) for better scalability and performance. For cold-start users, I'll combine popularity, genre diversity, and collaborative signals to satisfy your diversity requirements. The solution will include: • Personalized "Top Picks" and "Because You Watched" APIs. • Movie catalog, playback, user profiles, and recommendation integration. • Optimized recommendation service targeting sub-150 ms response times. • Clean, documented code with Git repository, setup guide, and model documentation. • Performance evaluation and hyperparameter documentation. Please visit my profile to review my AI, Machine Learning, and full-stack development projects. I'm currently offering an **introductory rate** for new clients while delivering production-quality, scalable solutions and long-term support. I'd be happy to discuss your dataset and recommend the best architecture for your platform. Best regards, Abdul Salam

@abishek129

Hi, strong match with my ML pipeline and backend experience. Model recommendation: model-based collaborative filtering (Matrix Factorization via ALS/SVD) over memory-based. At 100k users / 10k titles, KNN is too slow for <150ms P95 — MF gives pre-computable embeddings served from Redis in milliseconds. Cold-start (fewer than 5 ratings): hybrid using MF where available + popularity-weighted genre sampling to meet Shannon entropy ≥ 0.4. Backend: FastAPI serving /top-picks and /because-you-watched, pre-computed recommendations cached in Redis — P95 under 150ms comfortably achievable. Background Celery job recomputes as new ratings arrive. MF via Implicit or Surprise library. Streaming platform: catalog browsing, HLS video playback, basic user profiles and rating UI. Frontend: React — Netflix-style row layout for Top Picks and Because You Watched, updating as user rates. Deliverables: Git repo, setup instructions, README explaining model choice, hyperparameters, libraries, cold-start strategy. Relevant experience: built embedding-based recommendation engine (cosine similarity, ranked outputs), production ML pipelines with documented accuracy, FastAPI backends, Redis caching. What does your ratings dataset look like — explicit (1-5 stars) or implicit (watch history, clicks)?