probabilistic modelling for recommender PhD candidate ... · with internal state and world...
Transcript of probabilistic modelling for recommender PhD candidate ... · with internal state and world...
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Cybernetics, human-in-the-loop and probabilistic modelling for recommender systems
Eliezer de Souza da SilvaPhD candidate, Department of Computer Science, NTNUhttps://eliezersilva.blog/
Talk presented at BRAIN NTNU eventhttps://brainntnu.no/portfolio/brain-talks-big-data2-2/
2019
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Initial thoughts
“A human being should be able to change a diaper, plan an invasion, butcher a hog, conn a ship, design a building, write a sonnet, balance accounts, build a wall, set a bone, comfort the dying, take orders, give orders, cooperate, act alone, solve equations, analyze a new problem, pitch manure, program a computer, cook a tasty meal, fight efficiently, die gallantly. Specialization is for insects.”
― Robert A. Heinlein
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Initial thoughts• Plan• Reason• Execute• Evaluate• Control• Learn• Communicate with language• Sense• Creativity• Feel• Capacity for love/empathy• Social life
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Initial thoughts
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Cybernetics
“It is my thesis that the physical functioning of the living individual and the operation of some of the newer communication machines are precisely parallel in their analogous attempts to control entropy through feedback.”
Norbert Wiener
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What about a machine?
• Idea 1: substitute human labour• Idea 2: extend human capabilities• Not necessarily mutually exclusive• Extending modes of acting over the
environment
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Model of possible interactions
Environment
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Model of possible interactions
Environment
Multiple Feedback Loops
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Model of possible interactions
Environment
Multiple Feedback Loops
Low Hanging fruit: model each possible interactions / feedback loop
● Language● Image● Movements● Sounds● Text● Spatio-temporal
dynamics
● Prediction● Learning ● Control
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The (success) story so far:● Classification with lots of data and labels
○ Vision○ Text / language
● Combining simulation and learning:○ Recent advances of AlphaZero and AlphaGo
● Representation learning with lots of data:○ Machine Translation and word embedding
● Computational efficient inference / modelling○ Variational inference, stochastic gradient
descent, probabilistic programming
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Road ahead:
● Learning with little data and little amount of meta-information / labels○ Semi-supervised learning
● Fusion and multiple interactions○ Transfer learning○ Relationship learning○ Language(s) for logic + statistical learning
● Better use of causal / counterfactual models○ Combining probabilistic & neural models
with internal state and world simulation
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Cross-fertilization with neuroscienceKarl Friston: Free energy principle
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Some examples in recommender system research
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Basic setup● Prediction of unseen entries: recommendation of items to users given
user interaction with some items
User 1 User 2 User 3
Item 1 Item 2 Item 3 Item 4
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Poisson Matrix Factorization
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Joint modelling of user social network and item topic content● User social network
○ Homophily○ Item exposure positively influenced by peers
(positive “peer-pressure”)● Item content analysis
○ Enrich items latent factors with topic model○ Cold start items○ Preferences can be influenced by topics
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User 1 User 2 User 3
Item 1 Item 2 Item 3 Item 4 Item 5
Topics
User 4
User Social
Network
Adding context via shared factors
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[Topics, Words]
[Topics, Users]
[Items, Topics]
[Items, Words]
[Items, Users]
Observed
Latent
Content-based social Poisson factorization
Content-Based Social Recommendation with Poisson Matrix Factorization. Eliezer de Souza da Silva; Helge Langseth; Heri Ramampiaro. ECML-PKDD 2017.
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Item Recommendations
• Top-M items for each user:– Approximate expected value of user-item matrix for each
unseen item for ranking
Rud User preferences
Shared item topic
intensity
Item topic offset
Weighted sum of social network neighbors interactions with item
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Results
PoissonMF-CS (K =10) and Gaussian-based models
PoissonMF-CS (K =10) and other Poisson factorization models
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Hierarchical RNN and (time) Point process for multi-session rec.
Time is of the essence: A joint Hierarchical RNN and Point Process model for time and item predictions. Bjørnar Vassøy; Massimiliano Ruocco; Eliezer de Souza da Silva; Erlend Aune.. WSDM 2019
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Results
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Beyond prediction: feedback loops and bias in recommendation
How Algorithmic Confounding in Recommendation Systems Increases Homogeneity and Decreases Utility. Chaney et al. RecSys 2018
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Recommendation as Reinforcement Learning
DRN: A Deep Reinforcement Learning Framework for News Recommendation. Zheng et al. WWW2018.
Master thesis at Norwegian Open AI Lab:
- Olav Nymoan- Massimiliano Ruocco
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A society of human-machine cooperation?
● Internal simulation of human and other AI agents states
○ Theory of mind● Counterfactual reasoning● System 2 type of thinking and
reasoning● Empathy / emotional
understanding● Explicit connection of behavior,
purpose and teleology (Wiener et al)
○ Bias is present even in purpose-blind systems
○ AI Risks● Human-centric engineering
(Michael I. Jordan)○ statistical and
computational thinking and modelling for automatic decision-making
Augmenting Human Capabilities to New Dimensions. Harri Valpola (The Curious AI Company), talk at Slush 2017
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Conclusion
• Model including different sources of information are promising for designing new algorithms
• Computational efficient approximate inference is essential for scalable models
• Work with flexible probabilistic programming languages• Look ahead with a map of possible loops of
human-machine-environment interactions is a good strategy for advancing the area
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Nordic Probabilistic AI Summer School 2019• 1st week of June• Focus on probabilistic programming (Pyro), variational
inference methods (classic and modern variations), and deep generative networks.
• Lectures and tutorials• http://www.probabilistic.ai/• https://www.facebook.com/probabilisticai