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Topic-Specific Recommendation An Approach to Greater Prediction Diversity and Accuracy

Topic-Specific Recommendation An Approach to Greater Prediction Diversity and Accuracy. CS 345a. Minho Kim Brian Tran. Outline. Motivation Topic-Specific Recommendation Comparison to other methods A specific example Future. Problems w/ Recommendation. Prediction Diversity

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Topic-Specific Recommendation An Approach to Greater Prediction Diversity and Accuracy

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  1. Topic-Specific Recommendation An Approach to Greater Prediction Diversity and Accuracy CS 345a Minho Kim Brian Tran

  2. Outline • Motivation • Topic-Specific Recommendation • Comparison to other methods • A specific example • Future

  3. Problems w/ Recommendation • Prediction Diversity • Improved Accuracy • Maximize Long Tail recommendation • Possibly provide recommendations for less popular movies

  4. Topic-Specific Recommendation • Divide items into different topics (genre) • Find similar users within each topic • Provide recommendations for each topic (even unseen ones) • Recommendations should be: • more diverse • more accurate

  5. Comparison to Other Methods

  6. A More In-Depth Look… • In Amazon, we entered the following movies: • All were considered dramas

  7. The Results Amazon’s: Ours: One drama, but also comedy/romance! All were dramas…

  8. Rotten Tomatoes: Rotten Rotten Tomatoes: Rotten Rotten Tomatoes: Fresh Avg Rating: 3.4 Avg Rating: 3.5 Avg Rating: 3.6 User’s Rating: 4.0 Our Predicted Rating: 5.0 User’s Rating: 4.0 Our Predicted Rating: 4.0 User’s Rating: 4.0 Our Predicted Rating: 4.2 Futhermore No other methods recommended these movies

  9. Future Possibilities • Different type of dataset • Larger dataset (Netflix) • Try it on different topics • Handling new items and/or users

  10. Questions?

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