I’ve been testing a platform called Lumigo.tv, and it made me rethink how recommendation systems could work if they started from human emotion instead of metadata.
The core idea is simple: instead of browsing genres or relying on collaborative filtering, you tell the system what kind of feeling you want from a movie or series — “calm but not boring,” “dark but with humor,” “nostalgic in a warm way,” etc. The AI tries to interpret the emotional structure of the request and map it to titles in its catalog. It’s surprisingly different from typing keywords into a traditional recommender; some of the results feel uncannily aligned with the vibe rather than the category.
The platform itself is a mix of a mood-based search engine, a personal tracking tool, and a place to build curated lists. It’s not trying to be a streaming service but more like an interface layer on top of the chaos of modern content libraries. The database seems broad enough to avoid the usual “small pool” problem, and the UI encourages exploration without overwhelming you.
What caught my attention wasn’t the AI gimmick, but the design philosophy behind it. Most discovery tools assume that past behavior predicts future taste. Lumigo leans into something more fluid: people watch according to context, mood, time of day, emotional bandwidth, even weather. Traditional systems don’t capture those signals well, and mood-driven search is an interesting attempt to fill that gap.
There are areas where the cracks show. Mood parsing is not an exact science. Some prompts land perfectly, others feel like they’re interpreted too literally. The quality of recommendations clearly depends on how rich the metadata is behind the scenes, and that’s a massive ongoing effort. It also raises the question of whether mood-labeling at scale becomes noisy or inconsistent over time.
Still, as a product experiment, it’s refreshing. It feels closer to how people actually talk about movies in real life (“I want something cozy tonight”) rather than how platforms expect us to search (“Comedy → Subgenre → Runtime”). Whether systems like this become a serious alternative to more conventional recommenders is unclear, but it’s one of the first attempts I’ve seen that treats discovery as something emotional rather than purely statistical.
If nothing else, it’s an intriguing example of how a simple shift in the input paradigm can completely change the feeling of interacting with a huge content database.
I’ve been testing a platform called Lumigo.tv, and it made me rethink how recommendation systems could work if they started from human emotion instead of metadata.
The core idea is simple: instead of browsing genres or relying on collaborative filtering, you tell the system what kind of feeling you want from a movie or series — “calm but not boring,” “dark but with humor,” “nostalgic in a warm way,” etc. The AI tries to interpret the emotional structure of the request and map it to titles in its catalog. It’s surprisingly different from typing keywords into a traditional recommender; some of the results feel uncannily aligned with the vibe rather than the category.
The platform itself is a mix of a mood-based search engine, a personal tracking tool, and a place to build curated lists. It’s not trying to be a streaming service but more like an interface layer on top of the chaos of modern content libraries. The database seems broad enough to avoid the usual “small pool” problem, and the UI encourages exploration without overwhelming you.
What caught my attention wasn’t the AI gimmick, but the design philosophy behind it. Most discovery tools assume that past behavior predicts future taste. Lumigo leans into something more fluid: people watch according to context, mood, time of day, emotional bandwidth, even weather. Traditional systems don’t capture those signals well, and mood-driven search is an interesting attempt to fill that gap.
There are areas where the cracks show. Mood parsing is not an exact science. Some prompts land perfectly, others feel like they’re interpreted too literally. The quality of recommendations clearly depends on how rich the metadata is behind the scenes, and that’s a massive ongoing effort. It also raises the question of whether mood-labeling at scale becomes noisy or inconsistent over time.
Still, as a product experiment, it’s refreshing. It feels closer to how people actually talk about movies in real life (“I want something cozy tonight”) rather than how platforms expect us to search (“Comedy → Subgenre → Runtime”). Whether systems like this become a serious alternative to more conventional recommenders is unclear, but it’s one of the first attempts I’ve seen that treats discovery as something emotional rather than purely statistical.
If nothing else, it’s an intriguing example of how a simple shift in the input paradigm can completely change the feeling of interacting with a huge content database.