This work combines ideas from FastText and recommendation systems.
It aims to recommend top-k venues by utilizing the sequentiality feature of check-ins and a recent vector space embedding method, namely the FastText. In general, this work:
- Forms groups of check-ins
- Learns the vector space representations of the venues
- Utilizes the learned embeddings to make venue recommendations
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/2005.12982