Moodflix – a movie recommendation engine based on your mood

Hacker News

Last week I released Moodflix ( https://moodflix.streamlit.app ), a movie recommendation engine based to find movies based on your mood. Moodflix was created on top of a movie dataset of 10k movies from The Movie Database. I vectorised the films using Hugging Face's T5 model ( https://huggingface.co/docs/transformers/model_doc/t5 ) using the film's plot synopsis, genres and languages. Then I indexed the vectors using hnswlib ( https://github.com/nmslib/hnswlib ). LLMs can understand a movie's plot pretty well and distill the similarities between a user's query (mood) to the movie's plot and genres. I have got feedback from close friends around linking movies to other review sites like IMDB or Rotten Tomatoes, linking movies to sites to stream the movie and adding movie posters. I would also love to hear from the community what things you like, what you want to see and what things you consider can be improved.

Visit product View on Hacker NewsNov 9, 2023

Share card

Actual performance

6points

2comments

Did not reach leaderboard

Launch Intel predictions

Analyze your own launch →

Hacker NewsStrong engagement from HN community · Strong signals: ide, io · Missing: https docs, excited, just released

59%59% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

Product HuntOn track for Day 1 leaderboard · Strong signals: model, user, using · Missing: mac, agents, macos

53%53% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive

40%40% predicted probability of success on AppSumo, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal

39%39% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Indie HackersIH features products with proven revenue · Strong signals: created · Missing: supports, reddit linkedin, podcasting

36%36% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue

18%18% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto

1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

Similar products

I made a movie recommendation app based on your mood34%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

I made a movie recommendation app based on your mood

Hacker News163

Movie recommendation based on inferences by critics48%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Movie recommendation based on inferences by critics

Hacker News10

A distributed Recommendation Engine based on Redis + Ruby (and C)60%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

A distributed Recommendation Engine based on Redis + Ruby (and C)

Hacker News97

Metataste.com - Movie Recommendation Engine50%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Metataste.com - Movie Recommendation Engine

Hacker News8

A movie recommendation engine based on a short quiz38%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

A movie recommendation engine based on a short quiz

Hacker News2

I made a books recommendation app based on your mood45%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

I made a books recommendation app based on your mood

Hacker News173

Gitlove – Recommendation Engine for Programmers60%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Gitlove – Recommendation Engine for Programmers

Hacker News1

Recommendation Engine as a Redis Module39%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Recommendation Engine as a Redis Module

Hacker News2

Phonevice33%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Recommendation engine for smartphones

Indie Hackers1ai

Movie recommendation engine using RAG50%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Movie recommendation engine using RAG

Hacker News2

Browse all products Analyze your launch