1. Increasing Diversity Through Furthest
Neighbor-Based Recommendation
Alan Said, Benjamin Kille, Brijnesh J. Jain, Sahin Albayrak
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2. Agenda
 Problem
 Approach: k Furthest Neighbor
 Experimental settings
 Results
 Conclusions
 Discussion
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3. Problem: Missing diversity
• Accurate recommendations
• However, all items appear
similar
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4. Problem: Missing diversity
Intersection: Intersection:
Actors 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
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5. Desired features of Recommendations
 Reflect a user‘s preferences
 Correct ranking
 Novelty
 Serendipity Idea: Combining orthogonal
recommendation
 Diversity
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6. k Furthest Neighbor
dislike
like
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7. k Furthest Neighbor

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8. 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
Approaches: Evaluation:
– kNN Pearson  precision @N
– kNN cosine  recall @N
– kFN Pearson  overlap
– kFN cosine N ϵ {5; 10; 25; 50; 100; 200}
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9. Results I
 Precision @ N
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
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10. Results II
 Recall @ N
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
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11. Results III
 Overlap
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12. 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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13. Thanks for your attention!!!
 http://recsyswiki.com
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14. Contact
Benjamin Kille
Researcher of Competence Center +49 (0) 30 / 314 – 74 128
Information Retrieval +49 (0) 30 / 314 – 74 003
& Machine Learning benjamin.kille@dai-labor.de
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15. 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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