2. Summary
•Introduction
•Unimodal Recommender Systems
•Proposal
•Experiments and Results
•Conclusions
•Future Works
MULTIMODAL INTERACTIONS IN RECOMMENDER SYSTEMS: AN ENSEMBLING APPROACH
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
MULTIMODAL INTERACTIONS IN RECOMMENDER SYSTEMS: AN ENSEMBLING APPROACH 3
4. Introduction
•The proposal uses:
•Ensemble technique
•Multimodal interactions
•Unimodal algorithms
•To generate a more accurate list of recommendations optimized for the user
MULTIMODAL INTERACTIONS IN RECOMMENDER SYSTEMS: AN ENSEMBLING APPROACH 4
Figure 1. Interactionsbyusers
5. Unimodal Recommender Systems
•Each unimodalrecommender uses a single or a simple subset of types of user feedback to generate a list of items
•The set of unimodalrecommenders that are used by our algorithm are:
•Matrix Factorization (MF)
•BPR MF (Bayesian Personalized Ranking)
MULTIMODAL INTERACTIONS IN RECOMMENDER SYSTEMS: AN ENSEMBLING APPROACH 5
6. Unimodal Recommender Systems
•Matrix Factorization (MF)
•Matrix factorization techniques allow the discovery of latent features underlying the interactions between users and items
•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
MULTIMODAL INTERACTIONS IN RECOMMENDER SYSTEMS: AN ENSEMBLING APPROACH 6
7. 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 unimodalrecommenders
•Interactions used:
-Ratings assigned by users (1-5)
-Tags assigned (0 | 1)
-History View (0 | 1)
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8. Proposal
Figure 2. Framework
MULTIMODAL INTERACTIONS IN RECOMMENDER SYSTEMS: AN ENSEMBLING APPROACH 8
16. Experiments and Results
•Database:
HetRecMovielens2k:
800,000 ratings
10,000 tags
2,113 users
10,197 Movies
•EvaluationMetrics: Map@N; Prec@N; With: 10 crossfoldvalidationandAll-but-oneProtocol
•Recommendationlibrary:
MyMediaLite3.10
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17. Experiments and Results
MULTIMODAL INTERACTIONS IN RECOMMENDER SYSTEMS: AN ENSEMBLING APPROACH 17
Figura 4. ComparativeMAP@N
18. Experiments and Results MULTIMODAL INTERACTIONS IN RECOMMENDER SYSTEMS: AN ENSEMBLING APPROACH 18
Figura 5. ComparativePrec@N
19. 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
•Explicit feedback achieved the worst results using matrix factorization.
•Using the proposed ensemble algorithm, we achieved better results than the baselines
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20. Future Works
•MachineLearning Methods
•Extension of the learning algorithm BPR MF
•Group-basedtechniquesfor recommendation
•Usingclusteringalgorithms
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21. Multimodal Interactions in Recommender Systems: An EnsemblingApproach
ARTHUR FORTES E MARCELO G. MANZATO
{FORTES; MMANZATO}@ICMC.USP.BR