#From Word Embeddings to Item Recommendation

This work combines ideas from Word2Vec and recommendation systems:

• Uses non-textual features; the check-ins; for the embeddings
• Extracts user-user, user-item and item-item relations
• Integrates the extracted relations with different recommendation paradigms
  • Recommendation based on K nearest items
  • Recommendation based on N neighbors
  • Recommendation based on both K nearest items and N neighbors

Cite the following paper whenever all or any part of this code is used.

• Makbule Gulcin Ozsoy: From Word Embeddings to Item Recommendation. https://arxiv.org/abs/1601.01356