Categories:
TV Custom Recommendation UC
Global use case developed by Aura Platform Team that allows users to launch a custom recommendation based on Large Language Models (LLMs), through the use of ATRIA
Introduction
The TV Custom Recommendation use case is a global experience designed and developed by Aura Global Team that allows Telefónica customers to ask Aura for a TV recommendation based on their mood and likes, using a vocal interface.
Find additional information in the following documents:
- Discover the TV Custom Recommendation specifications and the particularities of the use case development.
- Discover which are the resources used by the TV Custom Recommendation use case.
- Find out the NLP configuration for each OB for the TV Custom Recommendation use case.
Specifications
Kernel API
In order to resolve the user’s request, Aura uses Video Contents normalized Kernel API.
Request-response model
TV custom recommendation UC is available for use in both the deprecated request-response model v1 and the current request-response model v3.
Available channels
The TV Custom Recommendation UC is available for STB channel both in Spain and in Brazil.
Custom recommendation features
Currently, the TV custom recommendation use case includes:
- A conversation flow to identify the user’s likes and mood
- Once all the information is captured and treated by the LLM, a search by topic is launched to the TV APIs
Use case development
The TV custom recommendation use case development includes these components:
Understanding features
- TV custom recommendation use case intent:
intent.tv.custom-recommendation
In order to understand users’ requests (utterances), Aura is trained with:
- NLP expression to recognize the user’s utterance and detect the user’s intention.
Use case logic
Once Aura has recognized the user’s utterance based on NLP components, the use case should be resolved based on:
-
Aura Bot dialog:
TV Custom Recommendation is built over the tv-custom-recommendation-v1.
Use case configuration
Check the section Configuration of the TV Custom Recommendation use case in order to know the required configuration for the TV Custom Recommendation experience for each OB.