Note: this portfolio project with limited features done by bit_guber.
This Project accomplish top 50 Movie suggest for users from previously liked movies by them with quick and efficiently as possible way. This Project Separate into two parts one for FrontEnd Website other for API endpoint.There will be Top 100 Popular Movies based on reviews count each movie in MovieLens Dataset which were collected 9,734 movies over various periods of time.
Live-demo here
Which seperate parts has individual Progress steps that carefully design for efficient and performance aspects.
Nowadays, They assist us by suggesting items that are supposed to be of interest to us and thus likely to be inspected, consumed, or purchased. Recommendations are typically designed to serve a specific purpose and create a specific value for the consumer and the provider.
the Effects and Business value of a deployed recommender system is determined by a variety of factors, including the application domain and, more importantly, the company’s business model. In this case, the goal could be to increase the number of time users spend using the service. Increased engagement is also a common goal for businesses with a flat-rate subscription model, as engagement is often viewed as a proxy for retention. A recommender’s impact can be more direct in other domains.
- Website Configuration visit
frontend folder - Suggestion producer visit
API folder - Trained notebook name is
movie-recommended-vectors.ipynb