Pandas

Code The code can be found within my github Repo There are other folders that are a work-in-progress for setting up the model registry with GCP. Overview I use a Two-Tower model to generate personalized book recommendations. During training, I include my own book interaction data so the model can learn a personalized User embedding that reflects my reading preferences. After training, I extract and store all learned item embeddings in a vector index to enable fast similarity search. At inference time, the system computes a user’s embedding vector and performs an inner product with the item embeddings in the index to generate similarity scores for each book. ...

Code The code can be found within this PDF Can also be found within this repo at static/images/wide_and_deep/wide_and_deep.pdf Overview This is a passion project I’ve been working on to deepen my understanding of recommender systems in my free time. While I’ve previously implemented simpler methods—such as matrix factorization or with basic content-based filtering, this project is my attempt to implement a more elegant solution inspired by this paper. Architecture Summary The model processes inputs related to user–item pairs and leverages two components: ...