Upwork home
Sign up

Hire the Best Collaborative Filtering Specialists

More than 3,000 reviews on G2
Rating is 4.5 out of 5.
4.5/5
of Upwork by G2 peer reviewers
Hire freelancers
Liaquat A.

Dahranwala, Pakistan

$5/hr
5.0
10 jobs

I’m Liaquat Ali, and I help AI teams and business owners save time, improve accuracy, and scale operations by providing high-quality data annotation and image labeling for computer vision projects, along with dependable general virtual assistant and executive virtual assistant support for daily operations. 🔹 Data Annotation, Image Labeling & Computer Vision I provide accurate and consistent data annotation, image labeling, and computer vision support for AI and machine learning projects. I follow detailed guidelines and deliver clean, well-structured datasets that improve model performance and training accuracy. I can help with: ✅ Data annotation for computer vision projects ✅ Image labeling (bounding boxes, polygons, keypoints, polylines) ✅ Image & video annotation ✅ Data tagging, classification, and dataset preparation ✅ Following annotation guidelines with high accuracy ✅ Preparing outputs in required formats (YOLO, COCO, Pascal VOC, CSV, or client formats) 🔹 General Virtual Assistant & Executive Virtual Assistant Support Alongside AI work, I work as a General Virtual Assistant and Executive Virtual Assistant, supporting businesses with daily operations, admin tasks, and data handling so nothing falls through the cracks. Virtual Assistant services include: ✅ CRM data entry & management ✅ Shopify product upload, updates & inventory management ✅ Excel & Google Sheets data entry, cleaning & formatting ✅ Web research & lead research ✅ File management & document organization ✅ Email support, task coordination & admin support ✅ Managing comments & DMs for Meta ads (Facebook & Instagram) 🧠 Tools & Work Style *Google Sheets, Excel, CRM systems *Shopify product management *Annotation tools (Label Studio, CVAT, VIA, LabelImg – as required by client) *Clear communication, fast response, and strict data confidentiality *Detail-oriented, consistent, and deadline-focused delivery ⭐ Why Clients Choose Me ✔️ Strong attention to detail for data annotation & image labeling ✔️ Reliable General Virtual Assistant & Executive Virtual Assistant support ✔️ Clean, accurate datasets for computer vision projects ✔️ Fast communication & on-time delivery ✔️ Flexible with long-term and short-term tasks If you’re looking for someone who can handle data annotation, image labeling, computer vision tasks, and also support your business as a general virtual assistant or executive virtual assistant, I’m ready to help. 📩 Send me your project details and let’s get started.

  • Data Annotation
  • Computer Vision
  • Object Detection
  • Image Annotation
  • Video Annotation
  • CVAT
  • Roboflow
  • Quality Assurance
  • Administrative Support
  • Virtual Assistance
  • Executive Support
  • Online Research
  • Accuracy Verification
  • Customer Support
  • Email Communication
Anam J.

Cincinnati, Ohio

$8/hr
5.0
21 jobs

Disorganized data costs you time. Bad training data costs you everything...! Hi, I'm Anam, based in Ohio, USA. And I fix both. A Data Entry and Annotation Specialist with 4+ years of experience helping businesses stay organized and AI teams build cleaner, more reliable datasets. Whether you need your spreadsheets cleaned up, your CRM updated, or your image datasets labeled for machine learning, you GOT it ✅ DATA ENTRY SPECIALIST DATA MANAGEMENT Data entry into Excel, Google Sheets, Airtable & CRMs Data cleaning, deduplication, validation & formatting PDF to Excel/Word conversion & bulk uploads Spreadsheet formulas, pivot tables & conditional formatting E-COMMERCE DATA ENTRY Product listing on Shopify, Amazon, eBay, Etsy & WooCommerce Bulk product uploads, title & description formatting CRM DATA ENTRY & MANAGEMENT HubSpot | Zoho | Salesforce | Pipedrive | GoHighLevel Contact management, pipeline updates & data segmentation Zapier integrations & basic automation setup Real Estate Data Entry WEB RESEARCH & LEAD GENERATION B2B/B2C lead list building & verified contact collection LinkedIn research & email list building (Hunter, Apollo) Market, product & competitor research ✅ DATA ANNOTATION & AI TRAINING IMAGE ANNOTATION Bounding boxes, polygons & polylines Semantic & instance segmentation Object detection, classification & keypoint labeling TEXT ANNOTATION Named entity recognition (NER) & sentiment labeling Intent classification & NLP dataset preparation Text tagging & categorization AUDIO & VIDEO ANNOTATION Transcription & speaker diarization Audio event tagging & classification Video frame-level object labeling TOOLS CVAT | Labelbox | Roboflow ✅ WHY CLIENTS KEEP COMING BACK High accuracy on both high-volume and detail-heavy tasks I follow guidelines closely and flag edge cases before they become problems Responsive, reliable, and easy to work with across time zones I treat your data well, whether it's a CRM or a training dataset, Cause it matters If your data is messy, incomplete, or holding your business back, let's fix that. Send me a message, and I'll respond.

  • Administrative Support
  • Virtual Assistance
  • Data Annotation
  • Image Annotation
  • Executive Support
  • List Building
  • Data Labeling
  • Data Collection
  • Data Entry
  • Copy & Paste
  • Lead Generation
  • File Management
  • Property Management
  • Labelbox
  • CVAT
Shahzeb A.

Riyadh, Saudi Arabia

$30/hr
5.0
29 jobs

Do you have an AI vision that needs to become a real, working product? I don't just build models; I engineer complete, scalable solutions that turn data into actionable insights and automation. For over five years, I've specialized in bridging the gap between cutting-edge Artificial Intelligence (AI) research and robust software that delivers real-world value. My core expertise lies in computer vision and machine learning, but my skill set is full-stack. This means I can own your project from the initial data pipeline, through model training and optimization, all the way to deploying a polished desktop application or a secure enterprise API. I thrive on building tools that work seamlessly for end-users, whether it's a retail manager, a traffic controller, or a sports coach. My strongest suit is developing intelligent systems that "see" and understand the world. I've built a retail analytics platform (CrowdIQ) that transforms standard CCTV into a source of business intelligence, tracking customer demographics and behavior. In the sports domain, I created PadelIQ, an analytics engine that uses computer vision to track player movement, posture, and court coverage from match footage, providing real-time coaching feedback. For public safety, I developed a traffic management system (OmniRoad AI) using advanced object detection for real-time accident and congestion monitoring. Beyond computer vision, I architect full-scale data science pipelines. A prime example is my telecom churn prediction project, where I built a machine learning model to identify at-risk customers and paired it with an interactive Power BI dashboard. This end-to-end approach—from data analysis to a clear visualization of insights—ensures the model's findings directly inform business strategy and retention actions. I also develop the tools and infrastructure that power AI applications. I've built secure, enterprise-grade systems like DevelmoGPT, a RAG-based LLM that allows for secure, semantic search over private company documents. From creating simple utilities like PDF-to-audio converters to designing complex role-based access systems, I ensure the foundation of any AI solution is reliable, secure, and maintainable. My process is collaborative and results-driven. I start by deeply understanding your business problem, not just the technical requirement. We'll then iterate through prototyping, development, and testing to ensure the final product not only meets specs but also delivers tangible ROI. I communicate clearly at every stage, providing demos and documentation so you're never in the dark. Let's connect. Share your project idea or challenge, and I'll provide a clear outline of how we can leverage AI, machine learning, or computer vision to build your intelligent solution. Click the invite button to start the conversation. /// The following is just for SEO. You can ignore it /// #computer vision #computer vision engineer #computer vision OpenCV #machine learning computer vision #deep learning computer vision #computer vision machine learning #machine learning python #nlp machine learning

  • Computer Vision
  • Machine Learning
  • Artificial Intelligence
  • Object Detection & Tracking
  • Data Analysis
  • TensorFlow
  • PyTorch
  • AI Development
  • Deep Learning
  • Natural Language Processing
  • Python
  • Neural Network
  • Data Science
  • Data Analytics
  • Retrieval Augmented Generation
Zakaria A.

Rabat, Morocco

$15/hr
5.0
24 jobs

Greetings, I am a Senior AI Engineer and Data Architect specializing in Machine Learning, Deep Learning, Federated Learning, LLMs, AI Automation, and Data Architecture. I focus on building scalable, secure, and production-ready AI and data solutions that solve real business problems. My core strength lies in designing end-to-end AI and data systems, from data ingestion, data modeling, and data processing to model development, deployment, automation, and business intelligence. I have strong experience in LLM-based RAG systems, computer vision, privacy-preserving AI using Federated Learning, and modern data platforms. I also work on enhancing AI security through Blockchain integration where applicable. - Key Expertise: * Data Architecture & Engineering * Data Lake, Data Warehouse, and Lakehouse Architecture * Data Modeling: Star Schema, Snowflake Schema * ETL/ELT Pipelines and Data Integration * Data Governance, Data Quality, and Metadata Management * Batch and Real-Time Data Processing * Big Data Platforms: Spark, Hadoop, Cloudera CDP * DataOps, CI/CD, and MLOps - Generative AI and Automation * LLMs, RAG systems, chatbots * AI workflow automation and integrations - Advanced AI * Federated Learning * Secure AI systems with Blockchain integration - Machine Learning * Regression models, Decision Trees, SVM * Ensemble methods: Random Forest, Gradient Boosting, XGBoost * Probabilistic and distance-based models: Naive Bayes, KNN - Deep Learning * ANN, CNN, RNN, LSTM, GAN * Model optimization and deployment - Computer Vision * Image classification, object detection, segmentation I am passionate about collaborating with clients to deliver robust, efficient, and future-ready AI and data solutions. If you are looking for a Senior AI Engineer and Data Architect who combines research-level expertise with real-world implementation, I would be happy to discuss your project.

  • Federated Learning
  • Machine Learning
  • Deep Learning
  • Generative Adversarial Network
  • Machine Learning Model
  • Artificial Intelligence
  • Deep Learning Modeling
  • Convolutional Neural Network
  • Computer Vision
  • Natural Language Processing
  • Reinforcement Learning
  • Blockchain
  • LLM Prompt Engineering
  • Retrieval Augmented Generation
Kim Dave T.

Cebu City, Philippines

$4/hr
5.0
5 jobs

With over 5 years of experience I specialize in annotating and labeling images for machine learning and AI applications. With a proven track record in creating accurate high-quality datasets across diverse domains.

  • Data Labeling
  • Image Classification
  • Image Segmentation
  • Data Annotation
  • Image Annotation
  • Autonomous Vehicles
  • Satellite Image
  • Computer Vision
  • Machine Learning
  • Data Processing
  • Data Cleaning
  • Data Curation
  • CVAT
  • LabelImg
  • Affiliate Marketing
Youness I.

Casablanca, Morocco

$20/hr
5.0
2 jobs

Need an AI Agent? I'll build you one that does more than talk, it takes action, calls your APIs, and handles the work instead of just answering FAQs Most teams don't have an "AI" problem. They have a "my people spend three hours a day on work a system could do" problem. So they buy a chatbot, it answers a few FAQs, takes no real action, and the manual work is still sitting there every morning. That's what I fix. 𝐖𝐇𝐀𝐓 𝐈 𝐁𝐔𝐈𝐋𝐃 ✅ Multi-agent automation of a real workflow : research, reporting, data entry, document generation — built around the task, not a flashy demo ✅ Document & data pipelines that extract, structure, validate and route : with checks so wrong numbers never slip through ✅ Agents that take action : call your APIs, update your CRM, write to your tools, not just chat ✅ RAG assistants grounded in your own documents, so answers are sourced, not hallucinated ✅ Private, on-premise deployments for teams that legally can't send data to OpenAI 𝐎𝐍𝐄 𝐄𝐗𝐀𝐌𝐏𝐋𝐄 I built a production AI assistant for a pension fund in a regulated, on-prem environment, grounded in their internal knowledge base, with guardrails and zero tolerance for hallucinated figures. I've since built multi-agent systems that draft a document, validate it against the rules, and correct themselves before a human ever reviews it, turning 8 hours of manual drafting and checking into minutes. 𝐇𝐎𝐖 𝐈'𝐌 𝐃𝐈𝐅𝐅𝐄𝐑𝐄𝐍𝐓 ✅ Weeks, not 6-month projects, you see a working system early, not a slide deck ✅ Production-grade, not a notebook that breaks in week one, with documentation and full handover ✅ You own everything, no lock-in, no dependency on me ✅ Master's in Machine Learning, I know what's under the hood, not just how to call an API ✅ On-prem & private deployment when your data can't leave the building 𝐓𝐎𝐎𝐋𝐒 Python, FastAPI, Agno, LangGraph, CrewAI, LangChain, vector DBs (pgvector, Chroma, Pinecone), React/TypeScript, OpenAI, Claude, and local LLMs (Ollama, Mistral, Llama). I fit the stack to what you already run, I never force a rebuild. 𝐖𝐇𝐎 𝐓𝐇𝐈𝐒 𝐈𝐒 𝐅𝐎𝐑 Teams and founders whose people are buried in repetitive, manual work, drafting documents, compiling research, moving data between tools, who want a system that does it, not another chatbot. Especially useful if you're in finance, healthcare, or the public sector and your data has to stay private. Tell me the workflow that's eating your team's time, and I'll tell you honestly whether an agent can do it, and exactly how I'd build it before you decide to hire me!

  • Artificial Intelligence
  • App Development
  • Web Development
  • AI Agent Development
  • AI App Development
  • AI Bot
  • AI Chatbot
  • AI Development
  • Large Language Model
  • Generative AI
  • Machine Learning
  • Natural Language Processing
  • Python
  • LangChain
  • FastAPI
  • Retrieval Augmented Generation
  • Chatbot Development
  • Automation
  • API Integration
  • Prompt Engineering

How it works

Post a job for free Post a job

Tell us what you need. Create your own job post or generate one with AI then filter talent matches.

Hire top talent fast

Consult, interview, and hire quickly, so you can meet the freelancers you're excited about.

Collaborate easily

Use Upwork to chat or video call, share files, and track project progress right from the app.

Payment simplified

Manage payments in one place with flexible billing options. Only pay for approved work, hourly or by milestone.

Don't just take our word for it

Collaborative Filtering FAQs

What is collaborative filtering?

When designing a recommendation system, there are two major ways to go about it. We’ve already talked about content-based filtering, but what do you do when your content is simply too massive or diverse to manually apply attributes? For that, there’s collaborative filtering, a technique that’s widely used across social media, retail, and streaming services. In this article, we’ll explore how collaborative filtering works, where it’s used, and what skills you might need to get started.

For all the sophisticated math and machine learning techniques involved, the concept behind collaborative filtering is pretty straightforward: It’s based on the idea that people who share an interest in certain things will probably have similar tastes in other things as well. You experience collaborative filtering first-hand every time you go online and see “Customers Who Bought This Item Also Bought,” or “Users like you also liked…”

Why collaborative filtering?

The main difference between collaborative filtering and content-based filtering is conceptual. Where content-based filtering is built around the attributes of a given object, collaborative filtering relies on the behavior of users. This approach has some distinct advantages over content-based filtering:

  • It benefits from large user bases. Simply put, the more people are using the service, the better your recommendations will become, without doing additional development work or relying on subject area expertise.
  • It’s flexible across different domains. Collaborative filtering approaches are well suited to highly diverse sets of items. Where content-based filters rely on metadata, collaborative filtering is based on real-life activity, allowing it to make connections between seemingly disparate items (like say, an outboard motor and a fishing rod) that nonetheless might be relevant to some set of users (in this case, people who like to fish).
  • It produces more serendipitous recommendations. When it comes to recommendations, accuracy isn’t always the highest priority. Content-based filtering approaches tend to show users items that are very similar to items they’ve already liked, which can lead to filter bubble problems. By contrast, most users have interests that span different subsets, which in theory can result in more diverse (and interesting) recommendations.
  • It can capture more nuance around items. Even a highly detailed content-based filtering system will only capture some of the features of a given item. By relying on actual human experience, collaborative filtering can sometimes recommend items that have a greater affinity with one another than a strict comparison of their attributes would suggest.

Two methods: user-item vs item-item

There are two approaches to collaborative filtering, one based on items, the other on users. Item-item collaborative filtering was originally developed by Amazon and draws inferences about the relationship between different items based on which items are purchased together. The more often two items (say, peanut butter and jelly) appear in the same shopping cart or user history, the “closer” they’re said to be to one another. So, when someone comes and adds peanut butter to their cart, the algorithm will suggest things that are close, like jelly or white bread, over things that aren’t, like motor oil.

User-item filtering takes a slightly different approach. Here, rather than calculating the distance between items, we calculate the distance between users based on their ratings (or likes, or whatever metric applies). When coming up with recommendations for a particular user, we then look at the users that are closest to them and then suggest items those users also liked but that our user hasn’t interacted with yet. So, if you’ve watched and liked a certain number of videos on Facebook, Facebook can look at other users who liked those same videos and recommend one that they also liked but which you might not have seen yet.

The important point here is that in both the examples above, the system has no idea why any of these items are related to one another, it only knows that they either show up in the same basket together or that they’re liked by people with similar preferences. In some cases, though, this can be a feature rather than a shortcoming, especially in cases where the items to be filtered are extremely heterogeneous, as in online retailers or social networks. (Note: This can also lead to some unanticipated situations, as when Amazon’s algorithm began unintentionally suggesting drug paraphernalia to users who bought a particular scale.)

How to calculate similarity?

The above descriptions are meant to be general overviews of how collaborative filtering techniques are typically implemented. Behind each implementation, there are a number of different techniques for measuring the similarity of two different items or users. Which one is right for a given recommender system depends on both the use case and the nature of the data involved.

When the data you’re working with is dense, a simple Euclidean distance measure can work. In reality, though, data (especially ratings) is often sparse. In these cases, cosine similarity is often used. There are other measures (Pearson correlation coefficient, k-nearest-neighbors, etc.). Fortunately, most of these functions are easily performed in Python (assuming you have the SciPy and scikit-learn libraries). Are you looking for a data scientist to build a recommendation engine? Hire a data scientist on Upwork today.

Challenges of collaborative filtering

  • Complexity and expense. Collaborative filtering algorithms can run into scalability problems when the number of users and items gets too high (think in tens of millions of users and hundreds of thousands of items), especially when recommendations need to be generated in real-time online. Potential solution: This is where distributed clusters of machines running Hadoop or Spark come in handy. Depending on your project, it may also be possible to calculate relationships offline overnight by way of batch processing, which makes serving recommendations much quicker even if they’re no longer being updated in real-time.
  • Data sparsity. Many user signals are ambiguous. Just watching a video doesn’t tell YouTube whether you liked that particular video or not, and just eating at a restaurant doesn’t tell Yelp whether you liked it or not. That’s why ratings are so important in collaborative-filtering systems. But users don’t rate every item they interact with, and many users don’t rate anything at all. Potential solution: Depending on the nature of the data, there may be proxy measures that can be used. Another common technique is to assume that missing reviews are equivalent to average reviews, though this is a very strong assumption in most cases.
  • The “cold start” problem. As we’ve seen, collaborative-filtering can be a powerful way of recommending items based on user history, but what if there is no user history? This is called the “cold start” problem, and it can apply both to new items and to new users. Items with lots of history get recommended a lot, while those without never make it into the recommendation engine, resulting in a positive feedback loop. At the same time, new users have no history and thus the system doesn’t have any good recommendations. Potential solution: Onboarding processes can learn basic info to jump-start user preferences, importing social network contacts.

Relevant skills and tech

Building a recommender system with collaborative filtering is a major project that involves both data science and engineering challenges. Solving these challenges may require expertise with data processing and storage frameworks like Hadoop or Spark. When it comes to implementing algorithms, data-oriented programming languages like Python, Java, and Scala support libraries that make it easy to perform an array of machine learning and statistical analysis tasks.

Find more freelancers

Similar Collaborative Filtering Specialist Skills

Top Countries for Collaborative Filtering Specialists