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Increasing Diversity Through Furthest Neighbor-Based Recommendation
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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Learning
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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