Hybrid-Recommender-System

Hybrid Recommendation System Project Overview:

1. User-Based Recommendation:

   • Analyzes preferences of similar users based on movies watched by a specific user.
   • Recommends movies liked by similar users.
   • Steps involved:
     • Preparation of data by creating a user-movie dataframe.
     • Determining movies watched by the target user.
     • Accessing data and IDs of other users who watched the same movies.
     • Identifying the most similar users to the target user.
     • Calculating weighted ratings and recommendation scores.
     • Providing top movie recommendations based on scores.

2. Item-Based Recommendation:

   • Suggests similar movies based on the characteristics of a particular film.
   • Steps involved:
     • Extracting the last highly rated movie by the user.
     • Preprocessing data including titles, genres, and timestamps.
     • Creating a user-movie dataframe.
     • Calculating correlation between movies.
     • Sorting and selecting top similar movies.
     • Providing top movie recommendations.

3. Hybrid Recommendation:

   • The hybrid system combines user-based and item-based recommendation methods.
   • User-based method relies on user similarity to recommend movies.
   • Item-based method suggests similar movies based on characteristics of a highly rated movie.
   • The system aims to provide a diverse and personalized recommendation experience.