Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Mention LLMs in recommenders blog #72

Open
wants to merge 1 commit into
base: main
Choose a base branch
from
Open

Mention LLMs in recommenders blog #72

Changes from all commits
Commits
Show all changes
1 commit
Select commit
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
4 changes: 2 additions & 2 deletions ...2023-07-21-tf-recommenders-encode-user-watch-history-using-openai-embeddings.md
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -21,7 +21,7 @@ In this digital age of endless possibilities and vast information, **recommendat

Recommendation systems are normally composed of multiple models either by combining their outputs or chaining the models in a pipeline. It is common to have a **retrieval model** that selects a few hundred or thousands of candidates from the complete set and then run a **ranking model** on these candidates to rank them.

In this blog, we will focus on the **retrieval model** part, with special emphasis on encoding the user watch history. For an introduction to recommendation engines and their importance, check out our [blog post](https://blog.xmartlabs.com/blog/recommendations-engines-what-they-do-and-why-they-are-useful/) on the matter.
In this blog, we will focus on the **retrieval model** part, with special emphasis on encoding the user watch history with the help of LLMs. For an introduction to recommendation engines and their importance, check out our [blog post](https://blog.xmartlabs.com/blog/recommendations-engines-what-they-do-and-why-they-are-useful/) on the matter.

### Two tower retrieval models

Expand Down Expand Up @@ -50,7 +50,7 @@ As for the user ID, it is possible to let the model learn an embedding for each

We **split the dataset by users**, taking a random set of 20k users as our validation dataset. This way, we can measure how well the model generalizes to users that are not in the dataset.

### Leveraging OpenAI API to get movie embeddings
### Leveraging LLMs to get movie embeddings

Using some of the columns of the movie metadata we can build a descriptive text for each movie like this:

Expand Down