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

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

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

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 MAERMSE< 0.5 DiffExact Match Topic Specific0.891.1475391579 Per-Item Average0.981.236309342 STI Pearson0.951.226791304 Non- Personalized 0.891.126905169 Optimal Constant Weight 1.161.4953201428

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

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

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

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

10. Questions?