E-commerce AI Recommendation Engine

@Microlent

Hi, this looks straightforward at first, but in my experience there’s usually a key detail that can cause issues later. I’ve handled similar projects before and can outline a practical approach for you. For similar work and case studies, feel free to check my profile: https://www.freelancer.com/u/microlent Let me know if you'd like me to walk you through the plan. ~ Rajesh

@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

@technologiesit

As an experience-packed Full Stack Developer and Digital Solutions Expert, I'm no stranger to complex projects and tight turnarounds. In fact, one of my many fortes is precisely that -- developing scalable, high-performing solutions for businesses looking to maximize their digital presence and drive ROI. When engaged, I commit to a project wholeheartedly, making sure to not only deliver on-time but also ensure every facet of the task is taken care of with utmost precision. Over the course of my 10+ years' career, I've successfully executed over 100 projects rooted in multiple technological domains such as website development, mobile app development, API development & integrations, and more. This versatile exposure has enabled me to gain a profound understanding and command over different technologies such as Python which you specifically mention preference for your project. Furthermore, having strategized and implemented sophisticated AI components like chatbots and tool integrations in previous projects has familiarized me with implicit feedback datasets and data analysis processes - which you'd be glad to know is in line with some of your preferences. What sets me apart is that despite having delivered a range of highly technical projects before my objective has remained constant- harnessing technology to create real value. I'm genuinely excited about the potential your e-commerce AI recommendation engine holds and would be honored to collaboratively turn it into reality.

@pkundu25

Hi there, I am an experienced AI Developer. Your project matches my background perfectly. I recently built a recommendation system for an eLearning platform, handling large volume of content assets and achieving metrics identical to your goals. Here is how I will deliver your pipeline: • Models: I will design a hybrid engine for eCommerce using LightFM for collaborative filtering and NLP on metadata for cross-sell bundles. • Offers: A contextual bandit model will serve dynamic, real-time discount offers based on live session behavior. • API & Cloud: I will package the pipeline into a FastAPI Docker container on AWS, ensuring sub-100ms API endpoints. • Handover: You will receive feature engineering scripts, model weights, Dockerfiles, and an MLOps monitoring plan. 1. Portfolio: https://www.freelancer.in/u/pkundu25?sb=t 2. Notable previous projects - AI Powered Copilot for Text2SQL Query - AI Agent for Risk Mitigation - Recommendation system for eLearning - ChatPDF - Multiclass Intent classification - Semantic search engine 3. Relevant Skills - Python - Django/Flask/React.js - Agentic AI - NLP/API integration - GPT4o/Gemini/Llama3.2/Mistral - Prompt Engineering - LangChain - MySQL/PostgreSQL - TensorFlow Quick questions: 1. Is your transaction log currently stored in SQL or NoSQL? 2. What is your target API latency for the storefront? Let's align on a call to kick this off immediately!

@KarthikTechCon

Hi, I’m Karthik, with 15+ years of experience in AI/ML, recommendation systems, and cloud-based solutions. I can build a scalable recommendation engine for your eCommerce platform that delivers: ✔ Personalized Top-N product recommendations ✔ Frequently Bought Together bundles ✔ Dynamic offer and discount suggestions ✔ REST/GraphQL APIs for storefront integration ✔ Dockerized deployment on AWS/GCP ✔ Model evaluation, monitoring, and retraining pipelines My approach uses a hybrid recommendation model (collaborative + content-based) to maximize accuracy and handle cold-start scenarios effectively. I’ll provide complete source code, model artifacts, deployment scripts, API documentation, and performance reports. I have experience developing AI-driven personalization and recommendation platforms that improve engagement, conversions, and revenue. Regards, Karthik

@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.

@shabahathabib

Hi, I will build a scalable AI recommendation engine that transforms your 15K SKU dataset into a real time personalization system driving product discovery and cross sell revenue. I will design a hybrid recommendation approach combining collaborative filtering and content based embeddings to generate top N recommendations, frequently bought together bundles, and dynamic personalized offer suggestions. The solution will be deployed through REST or GraphQL APIs so your storefront and marketing tools can easily integrate with it. I will also deliver a full evaluation report including precision, recall, lift over baseline, and an A B testing framework, along with retraining cadence and monitoring metrics. Final handover will include clean feature engineering scripts, trained model artifacts, Dockerized deployment, and clear documentation for future scaling. Can you confirm your preferred cloud platform and required response time for live recommendations? Regards, Shabahat.

@TechWagger2026

As a seasoned Full-Stack and Mobile App Developer with extensive experience in Data-driven development, I'm the perfect fit for your E-commerce AI Recommendation Engine project. Over my 14-year career, I've delivered over 400 successful projects focused on web and mobile app development, UI/UX design, as well as CRM solutions. Additionally, I have a solid background in AI and LLM, which is part of what you need for this recommendation engine solution. On top of all this, my meticulous and organized approach to project management ensures effective and timely delivery within tight schedules. I've designed end-to-end solutions for diverse industries similar to yours and can guarantee my competence in providing precise evaluation reports including metrics like precision/recall and lift over baseline. Trust me to deliver cleaned feature engineering scripts, model weights, deployment Dockerfile alongside comprehensive documentation to make future iterations straightforward for your team. Let's partner together to create a recommendation engine that genuinely grows your revenue!

@yasinu8

As a freelancer, I can offer you not just seven years of experience in web development and design but also a deep understanding of the eCommerce landscape. I've worked with a variety of clients, both big and small, in the retail sector, allowing me to navigate complex product catalogues like yours efficiently. Having collaborated with numerous clients to implement recommendation systems, I understand the finer nuances involved in data ingestion-through-live deployment pipeline ownership – a skill that is critical to bringing your project fruition. Regarding tech skills, Python and its associated libraries are my daily go-to tools, and with respect to machine learning, TensorFlow, PyTorch, and Scikit-learn are my preferred frameworks. Additionally,I have experience with cloud giants like AWS and GCP that would complement your current infrastructure setup for model training and API hosting. Moreover, implicit feedback datasets for matrix factorization or deep learning recommenders do not daunt me; rather they excite me as they open up opportunities to push the envelope further on personalization potential. I pride myself on being detail-oriented not just during delivery, but also during handover. Rest assured that you'll receive not only well-trained models, but also cleaned feature engineering scripts, thorough documentation on deployment processes and model weights in easy-to-follow formats.

@nexvative

Hello, Your project aligns perfectly with my experience in Machine Learning, Recommendation Systems, Python, and scalable AI deployment. I can build an end-to-end recommendation engine that transforms your transaction data and customer behavior into personalized product suggestions, dynamic offers, and high-converting cross-sell bundles. My proposed approach: • Analyze transaction history, product metadata, and customer profiles • Develop a hybrid recommendation system combining collaborative filtering, content-based filtering, and association-rule mining for "Frequently Bought Together" recommendations • Implement personalized offer generation using customer segmentation and purchase propensity models • Build REST APIs (FastAPI) or GraphQL endpoints for seamless integration with your storefront and marketing tools • Deploy using Docker on AWS/GCP with monitoring, retraining pipelines, and performance tracking Deliverables: ✔ Top-N personalized recommendations ✔ Frequently Bought Together engine ✔ Dynamic offer recommendation module ✔ Production-ready APIs ✔ Dockerized deployment package ✔ Feature engineering scripts and training pipeline ✔ Evaluation report with Precision@K, Recall@K, MAP, Lift analysis, and A/B testing framework ✔ Complete documentation and handover support

@fizaa170

Your project is interesting because you're not looking for a simple recommendation widget—you're looking for a production-ready recommendation engine that can directly impact revenue through personalization, cross-selling, and offer optimization. My approach would be to start by analyzing transaction history, customer behavior, and product metadata to determine whether a collaborative, content-based, or hybrid model provides the best performance. Given 15K SKUs and historical transactions, a hybrid recommendation architecture would likely deliver the strongest results while handling cold-start scenarios more effectively. Deliverables I can help with include: • Top-N personalized recommendations • Frequently bought together bundles • Dynamic offer generation logic • REST/GraphQL APIs • Dockerized deployment • Evaluation framework with precision, recall, lift, and A/B testing methodology • Retraining and monitoring strategy A few questions: • How many historical transactions are available? • Do you have clickstream/view data or only purchase history? • Is real-time recommendation generation required, or are near-real-time updates acceptable? I'd be happy to review the data structure and discuss the most effective architecture for deployment on AWS or GCP. Best regards, Muhammad Ahsan Khan

@bondoc0526

༺❖༻ Dear Client ༺❖༻ Thanks for posting this project. Your requirements perfectly match my experience in building scalable AI systems, recommendation engines, and production-grade ML pipelines for eCommerce platforms. I have strong experience working with Python-based machine learning stacks including Scikit-learn, PyTorch, and TensorFlow, as well as designing hybrid recommendation systems using collaborative filtering, content-based models, and embedding-based approaches. I also work with real-world transactional datasets, implicit feedback, and large SKU catalogs similar to your 15K product structure. For your project, I can design and implement a full recommendation pipeline starting from data ingestion and feature engineering through to model training, evaluation, and deployment. This will include top-N product recommendations, “frequently bought together” bundles, and personalized offer generation. I will also build clean REST APIs (or GraphQL if preferred) so your storefront and marketing systems can easily consume real-time recommendations. Along with this, I will provide proper evaluation metrics (precision/recall, lift, A/B testing plan) and a structured retraining strategy. Final delivery will include Dockerized deployment, clean codebase, model artifacts, and clear documentation for future scaling. Let’s connect and discuss your data structure so I can propose the most effective model approach. Best regards, Glenn Bondoc

@Abutalha5

Hi, I am Abutalha, with experience in Python, machine learning, recommendation systems, and API development. I have worked with TensorFlow, PyTorch, Scikit-learn, and cloud deployments to build scalable AI solutions from data processing to production APIs. I can develop a recommendation engine that delivers Top-N product recommendations, personalized offers, and frequently bought together bundles using collaborative, content-based, or hybrid approaches. The solution will include REST/GraphQL APIs, evaluation reports, Docker deployment, and clean documentation for easy future improvements. Could you share a little more about your transaction data structure and whether real-time user events (clicks, views, carts) are available? Best regards, Abutalha

@alluankit01

Hi, I can build the AI recommendation engine for your e-commerce store, covering data ingestion, feature engineering, model development, API deployment, evaluation, and handoff documentation. The best solution is to start with your 15K SKU catalogue, transaction logs, and customer profiles, then build a hybrid recommendation pipeline combining collaborative filtering, content-based product similarity, and basket analysis for “frequently bought together” bundles. This will allow the system to return Top-N recommendations, personalised offers, and cross-sell bundles that improve as new user behaviour is captured. I’m comfortable with Python, Scikit-learn, TensorFlow/PyTorch, recommendation systems, implicit feedback data, matrix factorisation, product similarity models, customer segmentation, REST/GraphQL APIs, Docker deployment, AWS/GCP hosting, monitoring, and A/B test planning. Deliverables will include: * Clean data ingestion and feature scripts * Recommendation model and training pipeline * Top-N, personalised offer, and bundle outputs * REST or GraphQL endpoints * Evaluation report with precision/recall and lift over baseline * Retraining and monitoring plan * Dockerfile, README, and model handoff assets I’ll focus on a practical, scalable engine that your storefront and marketing tools can query easily and that is designed to improve conversion and revenue. Best regards Ankit

@dushyant317

We have experience building AI-powered recommendation and personalization systems using Python, machine learning pipelines, API-driven architectures, and scalable cloud deployments, and can develop a production-ready recommendation engine with real-time personalization, bundle generation, evaluation metrics, and deployment assets designed for long-term growth and optimization.

@Apponn

Hey there, I read your project details and understand that you need an eCommerce recommendation engine that can analyze customer behavior, predict preferences, and deliver personalized product suggestions to improve engagement and sales. I have experience with AI/ML solutions, recommendation systems, data analytics, API integrations, and eCommerce platforms. I can build a scalable recommendation engine that integrates seamlessly with your existing platform and delivers accurate, data-driven recommendations. I can provide the best solution for your project according to your budget while ensuring quality work and timely delivery. Thanks Rohit

@mubashir021

Hello, your vision for a next-generation recommendation engine is exciting. I can help you build a system that delivers smart product recommendations and personalized offers in real time. I am Mubashir, a Full-Stack Developer, Engineer, and UI/UX Specialist with 6+ years of experience in developing scalable machine learning models. Your primary need is to create a recommendation model that efficiently serves personalized suggestions and bundles based on user behavior. - I'll analyze your existing product catalog and transaction logs to determine the best approach for the recommendation model. - I'll develop the model using Python with TensorFlow or PyTorch, generating top-N product recommendations and dynamic discount suggestions. - I'll create REST or GraphQL endpoints for easy querying by your development team. - I'll compile an evaluation report detailing precision, recall, lift over baseline, and an A/B test plan, along with monitoring metrics for model retraining. - I'll provide all handover assets, including cleaned feature engineering scripts and a concise README for future iterations. Even if you're still considering options, I can provide a FREE detailed quotation and project proposal with a suggested roadmap at no cost. You can use this proposal as a reference if you decide to work with someone else. My Portfolio: https://www.freelancer.com/u/mubashir021 Mubashir Ahmed

@shubhankar1967

Hi, I am an IITian with 10+ years of experience to develop similar projects ,I will build a hybrid recommendation system combining collaborative filtering (Surprise SVD++) and content-based approaches (sentence-transformers for product embeddings) with a two-tower neural network in PyTorch for real-time personalization. The pipeline will use Apache Airflow for daily batch model retraining, FastAPI + Redis for sub-100ms inference serving, PostgreSQL with pgvector for nearest-neighbor lookup, and deploy via Docker on AWS ECS with CloudWatch monitoring for drift detection. Kindly click on the chat button so I can share you my relavant projects. Lets connect

@rakhshanz3

Drawing on my 20 years of experience in AI and ML, I am more than prepared to tackle your project. My expertise ranges from core Python to deep learning with TensorFlow and PyTorch, all of which align perfectly with the technical foundation you expect for this recommendation engine. Throughout my career, I've developed multiple advanced digital products similar to what you're envisioning. For instance, I built AI-powered voice agents and chatbots for customer support, CRM integrations for automation, and full stack SaaS applications with scalable frontend-backend architecture; all key tenets of your e-commerce project! Given the tight timeline, my approach would be combining a collaborative filtering model with content-based filtering. This guarantees not only intelligent product recommendations but also dynamic personalized offers. I'll supplement these with "frequently bought together" bundles to enhance cross-sell opportunities. Additionally, my experience with evaluation metrics and A/B test planning ensures your project concludes with a comprehensive report detailing precision/recall and lift over baseline. My aim is to deliver a handover that simplifies future iterations. By hiring me, you're enlisting in not just an AI Specialist for this project, but a partner who will continue to fuel revenue growth through iterative refinements!