tensorflow-keras-scala VS metarank

Compare tensorflow-keras-scala vs metarank and see what are their differences.

tensorflow-keras-scala

Scala-based Keras API for the Java bindings to TensorFlow. Mirror of https://codeberg.org/sciss/tensorflow-keras-scala (by Sciss)

metarank

A low code Machine Learning personalized ranking service for articles, listings, search results, recommendations that boosts user engagement. A friendly Learn-to-Rank engine (by metarank)
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tensorflow-keras-scala metarank
1 13
1 1,992
- 0.8%
0.0 9.1
over 2 years ago 5 days ago
Scala Scala
GNU Lesser General Public License v3.0 only Apache License 2.0
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
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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.

tensorflow-keras-scala

Posts with mentions or reviews of tensorflow-keras-scala. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2021-11-27.
  • What libraries do you use for machine learning and data visualizing in scala?
    5 projects | /r/scala | 27 Nov 2021
    There are Java bindings for TensorFlow, but that's quite low level. I tried to see if I can get some Keras API for Scala, but I'm no expert and haven't had enough time to invest in this, so it's stuck in alpha. Maybe I develop it slow burning over the next year. A bit envious that Kotlin has a Keras-like library.

metarank

Posts with mentions or reviews of metarank. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2022-06-22.
  • Ask HN: Is it ethical for open-source projects to have usage analytics tracking?
    1 project | news.ycombinator.com | 29 Aug 2022
    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?

  • My Favorite Off-the-Shelf Data Science Repos, What Are Yours?
    3 projects | news.ycombinator.com | 22 Jun 2022
    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
    1 project | /r/MachineLearning | 26 Mar 2022
  • 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)
    1 project | /r/hnzh | 23 Mar 2022
  • Show HN: We made an open-source personalization engine
    1 project | /r/WhileTrueCode | 23 Mar 2022
    1 project | /r/patient_hackernews | 23 Mar 2022
    1 project | /r/hackernews | 23 Mar 2022
    7 projects | news.ycombinator.com | 23 Mar 2022
    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
    1 project | /r/kubernetes | 23 Mar 2022
    2 projects | /r/scala | 23 Mar 2022

What are some alternatives?

When comparing tensorflow-keras-scala and metarank you can also consider the following projects:

Smile - Statistical Machine Intelligence & Learning Engine

recommenders - Best Practices on Recommendation Systems

kotlindl - High-level Deep Learning Framework written in Kotlin and inspired by Keras

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.