DeepMatch | deep matching model library for recommendations | Recommender System library

 by   shenweichen Python Version: v0.3.1 License: Apache-2.0

kandi X-RAY | DeepMatch Summary

kandi X-RAY | DeepMatch Summary

DeepMatch is a Python library typically used in Artificial Intelligence, Recommender System, Deep Learning, Pytorch, Tensorflow applications. DeepMatch has no bugs, it has no vulnerabilities, it has build file available, it has a Permissive License and it has medium support. You can download it from GitHub.

DeepMatch is a deep matching model library for recommendations & advertising. It's easy to train models and to export representation vectors for user and item which can be used for ANN search.You can use any complex model with model.predict() .

            kandi-support Support

              DeepMatch has a medium active ecosystem.
              It has 1959 star(s) with 491 fork(s). There are 49 watchers for this library.
              It had no major release in the last 12 months.
              There are 31 open issues and 40 have been closed. On average issues are closed in 288 days. There are 4 open pull requests and 0 closed requests.
              It has a neutral sentiment in the developer community.
              The latest version of DeepMatch is v0.3.1

            kandi-Quality Quality

              DeepMatch has 0 bugs and 62 code smells.

            kandi-Security Security

              DeepMatch has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.
              DeepMatch code analysis shows 0 unresolved vulnerabilities.
              There are 0 security hotspots that need review.

            kandi-License License

              DeepMatch is licensed under the Apache-2.0 License. This license is Permissive.
              Permissive licenses have the least restrictions, and you can use them in most projects.

            kandi-Reuse Reuse

              DeepMatch releases are available to install and integrate.
              Build file is available. You can build the component from source.
              DeepMatch saves you 741 person hours of effort in developing the same functionality from scratch.
              It has 1710 lines of code, 105 functions and 30 files.
              It has high code complexity. Code complexity directly impacts maintainability of the code.

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            DeepMatch Key Features

            No Key Features are available at this moment for DeepMatch.

            DeepMatch Examples and Code Snippets

            No Code Snippets are available at this moment for DeepMatch.

            Community Discussions


            Removing duplicate dates in two arrays using lodash/fp
            Asked 2018-May-07 at 21:04

            I'm trying to take a more functional approach to displaying data and having trouble removing duplicate items from an array. What I'm trying to achieve is removing and/or merge items which are of the same date.

            I've tried using the _.uniqBy but it just returns the list untouched it's because it's a date object and therefore.

            new Date(2018, 4, 7) === new Date(2018, 4, 7) //false new Date(2018, 4, 7) == new Date(2018, 4, 7) //false

            is there a way to deepMatch using uniqBy or another method of merging or removing duplicates in a functional approach.

            note: objects/functions have been simplified to the problem.



            Answered 2018-May-07 at 21:04

            You can use _.unionWith(), and check for equality using _.isEqual():


            Community Discussions, Code Snippets contain sources that include Stack Exchange Network


            No vulnerabilities reported

            Install DeepMatch

            You can download it from GitHub.
            You can use DeepMatch like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.


            For any new features, suggestions and bugs create an issue on GitHub. If you have any questions check and ask questions on community page Stack Overflow .
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            gh repo clone shenweichen/DeepMatch

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