Users have different and dynamic novelty preferences. We show how to determine these preferences from users' recent consumption and provide an efficient adaptive novelty recommender.
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"I like to explore sometimes": Adapting to Dynamic User Novelty Preferences
1. “I like to explore sometimes”:
Adapting to Dynamic User
Novelty Preferences
Komal
Kapoor
Vikas
Kumar
Joe
Konstan
Paul
Schrater
Loren
Terveen
1Twitter: #adaNovR
2. In this paper:
predict novelty preferences based on past
consumption behavior.
users’ novelty preferences vary over time.
adaptive novelty recommender
2
6. Why we want to understand these
novelty preferences?
Explore Recommend
same old
Bored Churn
Exploit Recommend
New Items
Frustration Churn
6
7. Why we want to understand these
novelty preferences?
Explore Recommend
same old
Bored Churn
Exploit Recommend
New Items
Frustration Churn
7
8. Static Preference Models
Similar Users Have
Similar Preferences
Users Prefer
Similar Items
User-based Filtering Item-based Filtering
8
9. Static Preference Models
Similar Users Have
Similar Preferences
Users Prefer
Similar Items
User-based Filtering Item-based Filtering
No understanding of user
consumption behavior.
Fails when preferences change!!
9
10. Dynamic Novelty Preference
» not every user seek new
items
» some users seek (or
explore) more
» even they do it sometimes
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11. Dynamic Novelty Preference
» not every user seek new
items
» some users seek (or
explore) more
» even they do it sometimes
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Add value to user experience by
understanding their (changing) need
better
12. Data
» Music data:
• Closely related to human emotions and behavior
responses
• Low risk/cost of consumption
» Two Datasets:
NDA
12
15. User Timeline: Definitions
• Familiar Set:
• items recently consumed by user (within time
window T)
• Novel or New Set:
• New items consumed **compared to
previous familiar set (T-1)**
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16. User Timeline: Definitions
• Familiar Set:
• items recently consumed by user (within time
window T)
• Novel or New Set:
• New items consumed **compared to
previous familiar set** set.
Novelty Seeking Score (nvSeek) =
#new-items / #unique-items
16
17. In this paper:
predict novelty preferences based on past
consumption behavior.
users’ novelty preferences vary over time.
adaptive novelty recommender
17
18. Results (1):
Users have different novelty preferences
Novelty Seeking Score
NumberofUsers
• We have some high
as well as some low
novelty seeking
users.
• Scores vary across
the users ( s.d >
0, p-val ~ 0)
18
19. Results (2):
Users have dynamic novelty preferences
Seeking Deviation
NumberofUsers
• users’ seeking score
deviation across multiple
time window.
• users show dynamic
seeking score over a
period of three months
(Mean > 0, pval ~ 0)
19
20. In this paper:
predict novelty preferences based on past
consumption behavior.
users’ novelty preferences vary over time.
adaptive novelty recommender
20
21. Intuition
Diverse users are likely to be
more novelty seeking
Diversity of the familiar set
User bored with their current
selection are likely to be more
novelty seeking
Boredom with the familiar set
21
22. Model features:
» Diversity = more items, more diverse
» Boredom:
• Dynamic Item Preference [Kapoor et al, WSDM 2015]
– More you play, OR
– less gap between your plays
Fast to reach boredom
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24. Results:
» Accurate seeking score predictions than
constant novelty.
» Both features are significant and
positively correlated
• higher diversity seeking new items
• higher boredom seeking new items
24
25. In this paper:
predict novelty preferences based on past
consumption behavior.
users’ novelty preferences vary over time.
adaptive novelty recommender
25
37. Key Takeaways:
» Novelty Preferences are dynamic across
and within users
» Past consumption provides significant
signal to predict future novelty
preferences.
» A recommender capable to adapt to
novelty preference
37
38. Conclusion
» Modeling novelty preference dynamics
significantly impacts recommendation
design
» Future Work:
• Study the effect on retention due to adaptive
recommendations.
38
39. Danke!!
(thanks!!)
Supported by National Science Foundation under grants IIS 08-08692, IIS 09-
64695, UMN SOBACO grant and Doctoral Dissertation Fellowship.
Questions?
39
Notes de l'éditeur
----- Meeting Notes (9/16/15 14:43) -----
Explore Vs Exploit!!
Lean back users Vs no interruption required users
- One step closer to understand user behavior and able to make require changes in recommendations
- Team!! (PhD Candidate @ GroupLens at University of Minnesota)
- Paul - pysychological perspective
Most of you listen to music.
Raise your hand if you have been listening to same music playlist for over a month now?
Now, raise your hand if you try to change your playlist often searching for new or music from the past?
Most of you listen to music.
Raise your hand if you have been listening to same music playlist for over a month now?
Now, raise your hand if you try to change your playlist often searching for new or music from the past?
Most of you listen to music.
Raise your hand if you have been listening to same music playlist for over a month now?
Now, raise your hand if you try to change your playlist often searching for new or music from the past?
Now, if you
We confirm the results with the other data too. Details are in the paper