Transcript Topic Specific Recommendation

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
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Prediction Diversity
Improved Accuracy
Maximize Long Tail recommendation
–
Possibly provide recommendations for less
popular movies
Topic-Specific Recommendation
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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
MAE
RMSE
< 0.5 Diff
Exact
Match
Topic Specific
0.89
1.14
7539
1579
Per-Item Average
0.98
1.23
6309
342
STI Pearson
0.95
1.22
6791
304
NonPersonalized
0.89
1.12
6905
169
Optimal
Constant Weight
1.16
1.49
5320
1428
A More In-Depth Look…
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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
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
No other methods recommended these movies
Future Possibilities
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Different type of dataset
Larger dataset (Netflix)
Try it on different topics
Handling new items and/or users
Questions?