metarank
Medusa
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metarank | Medusa | |
---|---|---|
13 | 220 | |
1,981 | 22,799 | |
0.9% | 4.5% | |
9.1 | 9.9 | |
6 days ago | 7 days ago | |
Scala | TypeScript | |
Apache License 2.0 | MIT License |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
metarank
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Ask HN: Is it ethical for open-source projects to have usage analytics tracking?
We’re building an open-source tool to do search/category/recommendation personalization https://github.com/metarank/metarank, eventually planning to create a business out of it. We have a small number of pilot projects with real feedback, but we rarely have a chance to see how new people interact with the service, as it’s self-hosted backend tool with no UI.
We have an idea to add anonymous analytics reporting to get a glimpse of real usage (and places where people are struggling to improve), but are concerned if it’s ethical or not to do such intrusive things.
Is it acceptable for an open-source project to have this type of tracking, considering our materialistic plans to transform it into a business?
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My Favorite Off-the-Shelf Data Science Repos, What Are Yours?
Here are my top off-the-shelf data science models for Marketing. Would be interested which other marketing data science tools you use?
Product Recommendation on Your Website with Metarank (https://github.com/metarank/metarank)
Metarank is a tool that helps you easily build an advanced recommendation engine for your products or content on your website. To get started you only need historical performance data of your products (e.g. number of clicks) and additional metadata like product rating, genre, ingredients or price. In a YAML file, you define the features and the model parameters (e.g. number of iterations, modeling technique). The API service integrates with Apache Flink and can be easily integrated into Kubernetes clusters.
User Journey Analysis on your Website with Retentioneering (https://github.com/retentioneering/retentioneering-tools)
Retentioneering helps you to understand the user journey on your website. Retentioneering is a Python library that allows you to easily connect your Google Analytics data (in Bigquery). You define user-id, event-type and time stamp. From this data input a comprehensive graph network is created with gains and losses as you know it from a customer journey. In addition, customer segments are created that have a similar customer journey. This reduces the complexity of a purely descriptive view of the data.
Marketing Mix Modeling with Robyn (https://github.com/facebookexperimental/Robyn)
Less third-party cookie means less attribution models. The answer to this is Marketing Mix Modeling. Marketing mix models are regression models that use statistical probability to calculate the effect size of marketing channels and other independent variables. The advantage is that business context can be modeled much more realistically. For example, Google Searches for the own brand can be integrated to determine the share of the own brand strength in the revenue. Likewise, offline advertising measures can be modeled with other metrics in this context (e.g. offline advertising with GRPs). Robyn takes into account adstock effects, ROAS calculation and multicollinarity in the marketing channels. In addition, with simple functionality, budgets can be optimized using the predictions and results from marketing tests can be integrated into the model for calibration.
- [P] Metarank - A low code Machine Learning tool that personalizes product listings, articles, recommendations, and search results in order to boost sales. A friendly Learn-to-Rank engine
- Show HN: 我们做了一个开源的个性化引擎 (Show HN: We made an open-source personalization engine)
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Show HN: We made an open-source personalization engine
As people with heavy e-commerce background, we feel that the main pain point of typical old-school offline personalization solutions is that 80% of customers in medium-sized online stores are coming only once:
* you have a very short window to adapt your store, as the visitor will never come back in the future.
* even if you have zero past knowledge about a new visitor, there is still something to compare with other similar visitors: are they from mobile? Is it ios or android? Are they US? Is it a holiday now? Did they come from google search or facebook ad?
* this knowledge is ephemeral and makes sense only within their current session. But a visitor can still do a couple of interactions like browsing different collections of items or clicking on search results, and it can also be taken into account.
But compared to Amazon and Google, it's you who define which features should be used for the ranking and how long they are stored (see the "ttl" option on all feature extractors in our docs for details).
For example, here is https://github.com/metarank/metarank/blob/master/src/test/re... the config of features used in the movie recommendations demo - in a most privacy-sensitive setup you can just drop all the "interacted_with" extractors and will get zero private data stored for each visitor.
- Metarank - A low code Machine Learning tool that personalizes product listings, articles, recommendations, and search results in order to boost sales. A friendly Learn-to-Rank engine
Medusa
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How to build an eCommerce website and integrating Email notification only using open source tools
You can learn more about Medusa by checking their GitHub repository.
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MedusaJS: What we shipped in the past 12 weeks to our open-source commerce toolbox
Hello, I'm Nick, co-founder of Medusa. In keeping with our tradition, we are excited to share our progress on our open-source commerce SDK with this wonderful community. We eagerly want your feedback!
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How to Build an Electronic Commerce Store with Medusajs
If you have everything installed, follow these steps to set up your Medusa project.
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Mastering Cloud-Native E-commerce: A Deep Dive into Microtica and Medusa for Swift Deployment
As the world moves towards a more digital economy, e-commerce is becoming an increasingly important part of businesses. To keep up with the changing times, it’s essential to adopt a cloud-native approach to your e-commerce platform. In this blog, we will introduce you to two powerful tools that can help you achieve rapid deployment of your e-commerce website: Microtica and Medusa.js. We will take a deep dive into what cloud-native e-commerce is, how Microtica and Medusa.js work, and how they complement each other. We will also discuss case studies of successful deployment using these tools and what skills are required for implementing them. Lastly, we’ll talk about future trends in cloud-native e-commerce and how Microtica and Medusa.js shape the future of online shopping.
- Our Team's Favourite Open Source Projects Right Now
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Announcing Next.js Starter with App Router support
The Medusa Next.js Starter Template supports popular instant-search providers Melliseach and Algolia out of the box. With these integrations, you can provide fast and accurate search results.
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Serverless ecommerce with open-source modules [demo]
Co-founder of Medusa, here.
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Handle ecommerce product logic from a serverless Next.js function [demo]
Co-founder of Medusa here; building blocks for digital commerce.
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Open source Commerce SDK for Node.js developers
Hi - I'm Nick, co-founder of MedusaJS.
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Medusa Recap: What we shipped in the past 10 weeks to our Node.js commerce SDK
Nick, co-founder of Medusa, here. As per tradition now, we love to share the progress we make on our open-source commerce SDK with this amazing community. Always keen to hear your feedback!
What are some alternatives?
recommenders - Best Practices on Recommendation Systems
vendure - A headless GraphQL commerce platform for the modern web
retentioneering-tools - Retentioneering: product analytics, data-driven CJM optimization, marketing analytics, web analytics, transaction analytics, graph visualization, process mining, and behavioral segmentation in Python. Predictive analytics over clickstream, AB tests, machine learning, and Markov Chain simulations.
Saleor - Saleor Core: the high performance, composable, headless commerce API.
feathr - Feathr – A scalable, unified data and AI engineering platform for enterprise
Strapi - 🚀 Strapi is the leading open-source headless CMS. It’s 100% JavaScript/TypeScript, fully customizable and developer-first.
Robyn - Robyn is an experimental, AI/ML-powered and open sourced Marketing Mix Modeling (MMM) package from Meta Marketing Science. Our mission is to democratise modeling knowledge, inspire the industry through innovation, reduce human bias in the modeling process & build a strong open source marketing science community.
Vue Storefront - Alokai is a Frontend as a Service solution that simplifies composable commerce. It connects all the technologies needed to build and deploy fast & scalable ecommerce frontends. It guides merchants to deliver exceptional customer experiences quickly and easily.
eth-phishing-detect - Utility for detecting phishing domains targeting Web3 users
strapi-medusa-template
SynapseML - Simple and Distributed Machine Learning
Radarr - Movie organizer/manager for usenet and torrent users.