Apresentação de artigo WebMedia 2014

1. Ensemble Learning in Recommender Systems: Combining Multiple User Interactions for Ranking Personalization
ARTHUR FORTES E MARCELO G. MANZATO

2. Summary
•Introduction
•Unimodal Recommender Systems
•PreviousWorks
•Proposal
•Experiments and Results
•Conclusions
•Future Works
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INTERACTIONS FOR RANKING PERSONALIZATION 2

3. Introduction
•Increase in data on the Web (users, items, reviews)
•The traditional recommendation engines consist in acquiring the preferences:
•Implicit Feedback
•Explicit Feedback
•Literature reports a lack of techniques which integrate different types of user feedback into a generic model
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4. Introduction
•The proposal uses:
•Ensemble technique
•Multimodal interactions
•Unimodal algorithms
•To generate a more accurate list of recommendations optimized for the user
Figure 1. Interactionsbyusers
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5. Unimodal Recommender Systems
•Each unimodalrecommender uses a single or a simple subset of types of user feedback to generate a list of items
•Unimodal recommender that was used by our algorithm:
•BPR MF (Bayesian Personalized Ranking)
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6. Unimodal Recommender Systems
•BPR MF
•The BPR MF approach consists of providing personalized ranking of items to a user according only to implicit feedback (e.g. navigation, clicks, etc.)
•Considers positive and negative feedback
Figure 2. RepresentationBPR MF algorithm.
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7. Unimodal Recommender Systems
•Learning BPR MF
Algorithm1. Learning BPR MF algorithm.
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8. PreviousWorks
•The idea of using multiple interactions from users in recommendation systems by mean of ensemble methods has been explored in two previous work of ours:
•The first technique gave prominence to items that were assigned tags
•The second gave relevance to the items that were recommended in all interactions considered.
•In spite of their promising results, they were based on a set of heuristics:
•better on a restricted domain
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9. PreviousWorks
Algorithm2. Ensemble algorithmbasedonHeuristics.
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10. PreviousWorks
UserItem Score
5231 7.423
5 8 7.212
5123 6.232
....
20 33 6.823
20 8 6.112
20 54 5.232
...
N 1 8.423
N89 3.212
N 23 6.232
Algorithm2. Ensemble algorithmbasedonHeuristics.
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11. PreviousWorks
UserItem Score
5231 7.423
5 8 7.212
5123 6.232
....
20 33 6.823
20 8 6.112
20 54 5.232
...
N 1 8.423
N89 3.212
N 23 6.232
Algorithm2. Ensemble algorithmbasedonHeuristics.
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12. PreviousWorks
R(u, t)
5 231 7.4
5 8 7.2
5123 6.2
...
R(u, h)
5 8 8.7
5 325 8.2
552 7.8
...
R(u, r)
5 8 5
5 25 4.5
5572 4
...
R(u, partial)
U I S
58 ?
...
Algorithm2. Ensemble algorithmbasedonHeuristics.
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13. PreviousWorks
UserItem Score
5231 7.423
5 8 7.212
5123 6.232
AvgR = 6.9556
Algorithm2. Ensemble algorithmbasedonHeuristics.
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14. PreviousWorks
R(u, t)
5 231 7.4
...
R(u, h)
5 231 8.7
...
R(u, r)
5231 5
...
R(u, partial)
U I S
58 8.7
...
Avg(5, t) = 6.4
Avg(5, h) = 5.8
Avg(5, r) = 3
Algorithm2. Ensemble algorithmbasedonHeuristics.
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15. PreviousWorks
Algorithm2. Ensemble algorithmbasedonHeuristics.
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16. Proposal
•We propose a framework capable of generating recommendations based on multimodal user interactions (Positive and Negative)
•Post-processing step which combines classifications generated by different unimodalrecommender
•Interactions used:
-Tags assigned (0 | 1)
-History View (0 | 1)
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17. Proposal
Figure 2. ProposedFramework.
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18. Proposal
•Two rankings will be generated for each user. The equation which computes the weight of each pair (u,i), is defined as:
•Learning Weights
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19. Proposal
Algorithm3. ProposedAlgorithm.
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20. Proposal
Algorithm3. ProposedAlgorithm.
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21. Proposal
Algorithm3. ProposedAlgorithm.
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22. Experiments and Results
•Database:
HetRecLast FM 2k:
92,834user-listened artist relations
186,479 interactions tags applied
1,892 users
17,632 artists
•EvaluationMetrics: Map@N; Prec@N;
•With: 10 crossfoldvalidation
•All-but-oneProtocol
•Recommendationlibrary: MyMediaLite3.10
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23. Experiments and Results
Figure 3. ComparativeMAP@N
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24. Experiments and Results
Figure 3. ComparativeMAP@N
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25. Experiments and Results
Figure 4. ComparativePrec@N
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26. Experiments and Results
Figure 4. ComparativePrec@N
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27. Conclusions
•MAP has a tendency for higher values as the number of returned items increases while Precision has the opposite effect
•MAP only considers the relevant items and their positions in the ranking
•In Precision the order of items is irrelevant, the more items are filtered to the user, the more false positives may also be returned
•Using the proposed ensemble algorithm, we achieved better results than the baselines
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28. Future Works
•MachineLearning Methods
•Extension of the learning algorithm BPR MF
•Group-basedtechniquesfor recommendation
•Usingclusteringalgorithms
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29. Questions?
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30. Multimodal Interactions in Recommender Systems: An EnsemblingApproach
ARTHUR FORTES E MARCELO G. MANZATO
{FORTES; MMANZATO}@ICMC.USP.BR