Recommender-System

🎉 Welcome to the Movie Recommender System repository!

🎯 Focus: Building an efficient and accurate movie recommender system using item-based collaborative filtering.

🔍 Approach: Analyzing user-item interactions to establish item-item similarity for personalized movie recommendations.

📊 Data Source: Utilizing the MovieLens dataset for training and evaluation.

💻 Technology: Employing Python with Pandas and NumPy for data handling and analysis.

🎯 Objective: Providing insights into real-world recommender system challenges and showcasing collaborative filtering implementation.

🤝 Collaboration: Encouraging contributions and feedback to enhance the system further.

🎓 Research Internship at IIT BHU: Completed during my research internship at IIT BHU, with a focus on building the movie recommender system.

⚙️ Real-World Challenges: Gained valuable insights into real-world challenges and practical implementation of collaborative filtering algorithms.

Feel free to explore the code, contribute, and share feedback to improve the system further. Happy Recommending!

🧠📈 Through this internship, I gained invaluable experience in building a robust recommender system using item-based collaborative filtering, unlocking personalized user experiences. 🚀💡 The hands-on exposure with real-world datasets and cutting-edge algorithms further fueled my passion for machine learning and recommender systems.

SUMMARY

These files contain 1,000,209 anonymous ratings of approximately 3,900 movies made by 6,040 MovieLens users who joined MovieLens in 2000.

RATINGS FILE DESCRIPTION

All ratings are contained in the file "ratings.dat" and are in the following format:

UserID::MovieID::Rating::Timestamp

• UserIDs range between 1 and 6040
• MovieIDs range between 1 and 3952
• Ratings are made on a 5-star scale (whole-star ratings only)
• Timestamp is represented in seconds since the epoch as returned by time(2)
• Each user has at least 20 ratings

USERS FILE DESCRIPTION

User information is in the file "users.dat" and is in the following format:

UserID::Gender::Age::Occupation::Zip-code

All demographic information is provided voluntarily by the users and is not checked for accuracy. Only users who have provided some demographic information are included in this data set.

• Gender is denoted by a "M" for male and "F" for female

• Age is chosen from the following ranges:

  • 1: "Under 18"
  • 18: "18-24"
  • 25: "25-34"
  • 35: "35-44"
  • 45: "45-49"
  • 50: "50-55"
  • 56: "56+"

• Occupation is chosen from the following choices:

  • 0: "other" or not specified
  • 1: "academic/educator"
  • 2: "artist"
  • 3: "clerical/admin"
  • 4: "college/grad student"
  • 5: "customer service"
  • 6: "doctor/health care"
  • 7: "executive/managerial"
  • 8: "farmer"
  • 9: "homemaker"
  • 10: "K-12 student"
  • 11: "lawyer"
  • 12: "programmer"
  • 13: "retired"
  • 14: "sales/marketing"
  • 15: "scientist"
  • 16: "self-employed"
  • 17: "technician/engineer"
  • 18: "tradesman/craftsman"
  • 19: "unemployed"
  • 20: "writer"

MOVIES FILE DESCRIPTION

Movie information is in the file "movies.dat" and is in the following format:

MovieID::Title::Genres

• Titles are identical to titles provided by the IMDB (including year of release)

• Genres are pipe-separated and are selected from the following genres:

  • Action
  • Adventure
  • Animation
  • Children's
  • Comedy
  • Crime
  • Documentary
  • Drama
  • Fantasy
  • Film-Noir
  • Horror
  • Musical
  • Mystery
  • Romance
  • Sci-Fi
  • Thriller
  • War
  • Western