cnclabs/ICE
ICE: Item Concept Embedding
This toolkit helps you understand the conceptual similarity between items, like songs or movies, based on their descriptive text. You provide text describing various items and their associated concepts (keywords), and it generates numerical representations (embeddings) that allow you to find items or concepts that are conceptually similar, even if they are different types of entities. This is useful for data scientists, product managers, or content strategists looking to improve recommendation systems or content organization.
No commits in the last 6 months.
Use this if you need to compare different types of items (e.g., songs and artists, products and features) based on their underlying conceptual meaning derived from textual descriptions.
Not ideal if your items lack significant textual information for conceptual comparison or if you only need exact keyword matching.
Stars
89
Forks
8
Language
C++
License
—
Category
Last pushed
Nov 25, 2017
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/embeddings/cnclabs/ICE"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Higher-rated alternatives
Accenture/AmpliGraph
Python library for Representation Learning on Knowledge Graphs https://docs.ampligraph.org
benedekrozemberczki/graph2vec
A parallel implementation of "graph2vec: Learning Distributed Representations of Graphs"...
DeepGraphLearning/graphvite
GraphVite: A General and High-performance Graph Embedding System
bi-graph/Emgraph
A Python library for knowledge graph representation learning (graph embedding).
nju-websoft/muKG
μKG: A Library for Multi-source Knowledge Graph Embeddings and Applications, ISWC 2022