watching interstellar what you might like to read after · what if the companies want to venture...
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Heterogeneous Recommendations:What You Might Like To Read After
Watching Interstellar
Rachid Guerraoui, Anne-Marie Kermarrec, Tao Lin, Rhicheek Patra
EPFL, Switzerland
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Outline
● Introduction
● X-Map
○ X-Sim
○ AlterEgo
○ Recommendation
● Experiments & Conclusion
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Outline
● Introduction
● X-Map
○ X-Sim
○ AlterEgo
○ Recommendation
● Experiments & Conclusion
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Personalization
● Personalization services, mainly recommendations, are widely employed.
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Movies Music Books News
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Personalization
● Personalization services, mainly recommendations, are widely employed.
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Movies Music Books News
Most services are limited to personalization within a single domain
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Heterogeneous recommendations
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Movies Music
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Heterogeneous recommendations
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Movies Music
What if the companies want to venture across multiple domains?
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Heterogeneous recommendations: Scenario
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Heterogeneous recommendations: Scenario
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Given that Alice liked Interstellar, which books would she like to read?Given that Daniel like The Forever War, which movies would he like to watch?
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Why is heterogeneous recommendations a challenge?
● Quality○ Standard homogenous approaches do not work
○ Preferences of users vary across domains
○ Decrease in Density (user-item rating matrix) affects quality
■ Density: Fraction of actual interactions among all the possible user-item interactions
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Why is heterogeneous recommendations a challenge?
● Quality○ Standard homogenous approaches do not work
○ Preferences of users vary across domains
○ Decrease in Density (user-item rating matrix) affects quality
■ Density: Fraction of actual interactions among all the possible user-item interactions
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Books Movies Books + Movies
0.0204% 0.0569% 0.0147%
Density of domains in Amazon
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Why is heterogeneous recommendations a challenge?● Privacy
○ General concern in homogenous recommenders trivially extends to heterogeneous ones.○ Higher privacy concern in heterogeneous scenario due to an increase in the connections across
domains.[1]
○ Straddlers, i.e., users who connect multiple domains, are at a higher privacy risk.
1. Ramakrishnan et al. "Privacy risks in recommender systems." IEEE Internet Computing 5.6 (2001): 54.6
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Why is heterogeneous recommendations a challenge?● Scalability
○ Increase in information Increased computations Requires better scalability○ Extend to multiple domains (movies, books, songs, electronics)○ Additional computational overhead due to privacy preservation techniques
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Challenges for heterogeneous recommendations
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PrivacyScalabilityQuality
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Challenges for heterogeneous recommendations
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PrivacyScalabilityQuality
How to design a heterogeneous recommender to address these challenges?
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Challenges for heterogeneous recommendations
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PrivacyScalabilityQuality
X-Sim AlterEgo Differential
Privacy
X-Map
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Outline
● Introduction
● X-Map
○ X-Sim
○ AlterEgo
○ Recommendation
● Experiments & Conclusion
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X-Sim: Baseline Similarity Graph Construction● We use adjusted-cosine similarity to build this graph
● Any two items are connected if they have common users
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X-Sim: Baseline Similarity Graph Construction● We use adjusted-cosine similarity to build this graph
● Any two items are connected if they have common users● We extend the current similarities using meta-paths
○ Meta-paths connect heterogenous items e.g., movies, books, songs● Meta-paths captures more heterogeneous similarities
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X-Sim: Layer-based K-NN
● Multiple meta-paths are possible in a heterogeneous graph● K-NN connections across layers
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X-Sim: Layer-based K-NN
● Multiple meta-paths are possible in a heterogeneous graph● K-NN connections across layers
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X-Sim: Meta-path based similarities
● Weighted Significance (adjacent items): Mutually agreeing (like/dislike) users○ Higher number of users implies more significance
○ Normalized weighted significance
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X-Sim: Meta-path based similarities
● Weighted Significance (adjacent items): Mutually agreeing (like/dislike) users○ Higher number of users implies more significance
○ Normalized weighted significance
● Meta-path-based similarity: Baseline similarity weighted with significance
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X-Sim: Path Certainty
● Path Certainty: Captures the importance of paths.○ Longer paths are considered to be less important than shorter ones[1]
13[1]. Ramakrishnan et al. "Privacy risks in recommender systems." IEEE Internet Computing 5.6 (2001): 54.
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X-Sim: Cross-domain similarities
● For any two items i and j, there are multiple meta-paths between them○ Each meta-path has similarity (sp) and certainty (cp) values
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X-Sim: Cross-domain similarities
● For any two items i and j, there are multiple meta-paths between them○ Each meta-path has similarity (sp) and certainty (cp) values
● X-Sim: Cross-domain similarities between two heterogeneous items i and j.
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Outline
● Introduction
● X-Map
○ X-Sim
○ AlterEgo
○ Recommendation
● Experiments & Conclusion
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AlterEgo generation
● Create an AlterEgo profile of the user in the target domain
Alice’s AlterEgo profile (in target domain) mapped from her original profile (in source domain).
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AlterEgo generation
● Create an AlterEgo profile of the user in the target domain
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Alice’s AlterEgo profile (in target domain) mapped from her original profile (in source domain).
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AlterEgo generation (Private)
● Use probabilistic replacement (exponential mechanism for differential privacy[2])
Alice’s private AlterEgo profile (in target domain) mapped from her original profile (in source domain).
Private Replacement
Private Replacement
Private Replacement
[2]. Dwork, Cynthia, and Aaron Roth. "The algorithmic foundations of differential privacy." Foundations and Trends® in Theoretical Computer Science 9.3–4 (2014): 211-407. 17
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Outline
● Introduction
● X-Map
○ X-Sim
○ AlterEgo
○ Recommendation
● Experiments & Conclusion
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Recommendation: Algorithms
● Any homogenous algorithm can be applied due to AlterEgos in X-Map. ● X-Map currently supports:
○ User-based collaborative filtering○ Item-based collaborative filtering
● Temporal dynamics○ AlterEgos preserve the temporal pattern○ Capturing preference change of users○ More accurate recommendations[3]
19[3]. Koren, Yehuda. "Collaborative filtering with temporal dynamics." Communications of the ACM 53.4 (2010): 89-97.
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Recommendation: Privacy
● Within-domain privacy-preserving algorithms in X-Map○ -differential privacy based on recommendation-aware sensitivity.○ Supports both user-based and item-based algorithms
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Framework
X-Sim
Recommendation
AlterEgo
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Outline
● Introduction
● X-Map
○ X-Sim
○ AlterEgo
○ Recommendation
● Experiments & Conclusion
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Experimental SetupDatasets: Amazon movies and books
Framework: Apache Spark
Metric: Mean Absolute Error
Domain User Items Ratings
Movies 473,764 128,402 1,671,662
Books 725,846 403,234 2,708,839
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Quality: Accuracy comparison
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Quality: Impact of training set
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Quality: Impact of training set
Item-to-item similarities are more static than user-to-user similarities
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Privacy
Item-based approach User-based approach
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Privacy
Item-based approach User-based approach
1. Differential privacy: Lower leads to better privacy2. Higher privacy leads to lower quality due to noise addition.
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Temporality
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Temporality
● AlterEgos preserve temporal behavior of users.
● A significantly higher value of affects negatively due to users
with small profiles.
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Homogeneous scenario (Movielens)
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Scalability
Scales sub-linearly with multiple machines
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Summary
● X-Map: Heterogenous recommender that provides○ Quality (X-Sim, Temporality)○ Privacy○ Scalability○ Prototype: http://x-map.work/
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Summary
● X-Map: Heterogenous recommender that provides○ Quality (X-Sim, Temporality)○ Privacy○ Scalability○ Prototype: http://x-map.work/
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Quality: Impact of Sparsity