embedding-based item nearest neighborhoods extraction
Project description
DeepNeighbor
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.
It mainly has two parts : Embed step and Search step by the following codes:
model.train()，which generates embeddings for users and items (Deep),
model.search(), which looks for Approximate nearest neighbor for seed user/item (Neighbor) .
Install
pip install deepneighbor
How To Use
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
- word2vec
- Siamese Network with triple loss
- deepwalk
- graph convolutional network
- matrix factorization
- graph attention network
Examples
TBD
License
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Source Distribution
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