deepneighbor 0.2.6

embedding-based item nearest neighborhoods extraction

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