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

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Presentation transcript:

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

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

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

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

Comparison to Other Methods MAERMSE< 0.5 DiffExact Match Topic Specific Per-Item Average STI Pearson Non- Personalized Optimal Constant Weight

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

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

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

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

Questions?