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Increasing Diversity Through Furthest
Neighbor-Based Recommendation
Alan Said, Benjamin Kille, Brijnesh J. Jain, Sahin Albayrak
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Agenda
Problem
Approach: k Furthest Neighbor
Experimental settings
Results
Conclusions
Discussion
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Problem: Missing diversity
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• Accurate recommendations
• However, all items appear
similar
Problem: Missing diversity
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Intersection:
Actors
Intersection:
Plot
Harry Potter and the Chamber of Secrets 49,0% 32,3%
Harry Potter and the Prisoner of Azkaban 52,9% 26,7%
Harry Potter and the Goblet of Fire 39,2% 29,2%
Harry Potter and the Order of the Phoenix 49,0% 16,8%
Harry Potter and the Half-Blood Prince 41,2% 15,5%
Harry Potter and the Deathly Hallows: Part 1 43,1% 16,8%
Harry Potter and the Deathly Hallows: Part 2 49,0% 22,4%
Source: imdb.com
Desired features of Recommendations
Reflect a user‘s preferences
Correct ranking
Novelty
Serendipity
Diversity
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Idea: Combining orthogonal
recommendation
k Furthest Neighbor
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dislike
like
k Furthest Neighbor
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Experimental settings
Data set: randomly sampled 1 million ratings out of
MovieLens (1M100k)
excluded: 100 most popular movies (rating frequency)
excluded: users with < 40 ratings
44,214 users; 9,432 movies
Evaluation:
precision @N
recall @N
overlap
N ϵ {5; 10; 25; 50; 100; 200}
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Approaches:
– kNN Pearson
– kNN cosine
– kFN Pearson
– kFN cosine
Results I
Precision @ N
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N 5 10 25 50 100 200
Pearson Similarity 0,0007 0,0110 0,0170 0,0280 0,0410 0,0900
Cosine Similarity 0,0050 0,0070 0,0160 0,0270 0,0570 0,0000
Results II
Recall @ N
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N 5 10 25 50 100 200
Pearson Similarity 0,0080 0,0130 0,0210 0,2300 0,0140 0,0100
Cosine Similarity 0,0020 0,0060 0,0070 0,0060 0,0050 0,0040
Results III
Overlap
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Conclusion
„The enemy of my enemy is my friend“ seems to hold in
the context of recommender systems
kFN achieved worse precision
kFN provided higher recall with N > 50
kFN did provide orthogonal recommendations
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Thanks for your attention!!!
http://recsyswiki.com
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Contact
Benjamin Kille
Researcher of Competence Center
Information Retrieval
& Machine Learning
+49 (0) 30 / 314 – 74 128
+49 (0) 30 / 314 – 74 003
Discussion
How to optimize the approach?
Are there other ways to introcude more diverse
recommendations?
How to evaluate diversity in the context of recommender
system?
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