"I like to explore sometimes": Adapting to Dynamic User Novelty Preferences
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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
- 3. What are Preferences? Preferences determine how we make our everyday choices… 3
- 4. What are Preferences? Preferences determine how we make our everyday choices… Wait!! Everyone knows this! 4
- 5. What are Preferences? Novelty Preferences determine how well we appreciate new items.. Novelty Source: tumblr 5
- 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 10
- 11. Dynamic Novelty Preference » not every user seek new items » some users seek (or explore) more » even they do it sometimes 11 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
- 13. Details: last.fm last.fm Duration 3 months Number of Users 882 Avg. #session per user 56 Avg. session Length (#items) 39 13
- 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)** 15
- 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 22
- 23. Model » Logistic Regression Model: • prediction novelty preference score based on past consumptions 23
- 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
- 26. Adaptive Recommendation » Existing Systems: » Adaptive System: Novelty Seeking Score 26
- 28. Design Adaptive Recommendation Module Novelty Seeking Prediction Module Item Ranking Module User Timelines (Past Sessions) Ranked Familiar Items Ranked Novel Items Consumed/ Rated Items Behavioral/ Session Attributes Novelty Seeking Input Output OutputInput Top-N Recommendations - Item1 - Item2 - ItemN Current Session Recommendations System Design Adaptive Ranking Explicit Implicit 28
- 29. Evaluation » Baselines • Item Based CF • Constant Novelty • PureN : only novel or new items • PureF : only familiar items 29
- 30. Metrics » Metrics • Recommendation Accuracy • cost sensitive weighted F-measure • Novelty Accuracy: • new items recommended Vs. new items consumed 30
- 31. Results Recommendation Accurracy Last.fm Weighted F-measure for different novelty seeking score 31
- 32. Results Recommendation Accurracy Last.fm Weighted F-measure for different novelty seeking score Performance of PureF and Item Based declines as novelty seeking score increases 32
- 33. Results Recommendation Accurracy Last.fm Weighted F-measure for different novelty seeking score adaNov-R performs comparable to the best baseline for all novelty seeking scores 33
- 35. Novelty Accuracy adaNov-R capable to adapt to number of new items. 35
- 36. Novelty Accuracy PureN provides all new items. Item based CF rarely provides new items 36
- 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
Editor's Notes
- ----- 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