Metadata-Version: 2.1
Name: deepneighbor
Version: 0.2.6
Summary: embedding-based item nearest neighborhoods extraction
Home-page: https://github.com/LouisBIGDATA/deepneighbor
Author: Yufeng Wang
Author-email: louiswang524@gmail.com
License: UNKNOWN
Description: # DeepNeighbor
        <p align="center">
          <img src="deepneighbor_logo.png"/>
        </p>
        
        [![Python Versions](https://img.shields.io/pypi/pyversions/deepneighbor.svg)](https://pypi.org/project/deepneighbor)
        [![PyPI Version](https://img.shields.io/pypi/v/deepneighbor.svg)](https://pypi.org/project/deepneighbor)
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        ---
        
        DeepNeighbor is a **High-level**,**Flexible** and **Extendible** package for embedding-based information retrieval from user-item interaction logs. Just as the name suggested, **'deep'** means deep learning models to get user/item embeddings, while **'neighbor'** means approximate nearest neighbor search in the embedding space.<br>
        It mainly has two parts : Embed step and Search step by the following codes:<br>
        <br>`model.train()`，which generates embeddings for users and items (Deep),
        <br> `model.search()`, which looks for Approximate nearest neighbor for seed user/item (Neighbor) .
        <br>
        
        ### Install
        ```python
        pip install deepneighbor
        ```
        ### How To Use
        
        ```python
        from deepneighbor.embed import Embed
        
        model = Embed(data)
        model.train()
        model.search(seed = 'Louis', k=10)
        ```
        ### Input format
        The input data for the **Embed()** should be a pandas DataFrame with two columns in order: 'user' and 'item'. For each user, the item are recommended to be ordered by time.
        ### Models
        - [x]  word2vec
        - [ ] Siamese Network with triple loss
        - [ ]  deepwalk
        - [x]  graph convolutional network
        - [ ]  matrix factorization
        - [ ]  graph attention network
        
        ### Examples
        TBD
        
        ### License
        
Keywords: embedding,information retrieval,deep learning,torch,tensor,pytorch,nearest neighbor
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Education
Classifier: Intended Audience :: Science/Research
Classifier: Topic :: Scientific/Engineering
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: Topic :: Software Development
Classifier: Topic :: Software Development :: Libraries
Classifier: Topic :: Software Development :: Libraries :: Python Modules
Requires-Python: >=3.4
Description-Content-Type: text/markdown
