introduction and new trends in recommender systems
TRANSCRIPT
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Introduction and new trends in Recommender Systems
Paolo Tomeo@paotomeo
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Information overload
@mkapor
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www.smartinsights.com/internet-marketing-statistics/happens-online-60-seconds
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www.smartinsights.com/internet-marketing-statistics/happens-online-60-seconds
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Recommender Systems“Software tools and techniques that provide suggestions for items that are most likely of interest to a particular user”
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User – Item Interaction
Rating
Like
Click
Visualization
Search query
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Ratings
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Diversity matters
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Suggerisce all'utente item simili a quelli che ha apprezzato in passato
Approaches
Content Based filtering
Collaborative filtering
Hybrid approaches
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Suggerisce item apprezzati da altri utenti che hanno preferenze simili
Content based filtering
Recommendations based on items similar to the ones that the user liked in the past
Strengthsuser independence
explainabilityuseful for cold-start
Drawbackssensitive to bad or incomplete information
over-specializationless novelty and discovery
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Suggerisce item apprezzati da altri utenti che hanno preferenze simili
Collaborative filtering
Recommendations based on items that other users with similar tastes liked in the past
Strengthsindependent from the content
typically more accuratecan promote discovery
Drawbackssensitive to the quantity of users and feedbacks
difficult to recommend new item (cold-start item)can reinforce item popularity
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Suggerisce item apprezzati da altri utenti che hanno preferenze simili
Matrix factorization CF
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Hybrid approaches
Combination of content-based and collaborative filtering methods
Ensemble of different methods Graph-based methods applied on heterogeneous networks
Feature combination -> (Matrix Factorization with side information, Factorization Machines, Neural Networks, …)
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Beyond accuracyDiversity
Novelty
Serendipity
Explanation
Trust
Performance
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Offline evaluation
1 - Choose a dataset2 - Split feedbacks for each user in train, validation and test sets
3 - Train the systems with the evaluation set
4 - Produce the recommendations5 - Evaluate on the test set
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Some Libraries
RankSys - Java 8 Recommender Systems framework for novelty, diversity and much morehttps://github.com/RankSys/RankSys
Rival - Java toolkit for recommender system evaluationhttps://github.com/recommenders/rival
GraphLab Create - Python machine learning frameworkhttps://turi.com/products/create
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(Some) New trends
Deep learning
Wide and deep learning
Multi-criteria
Graph-based algorithms
Use of Semantic Web
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Deep learning
P. Covington, J. Adams, E. Sargin. “Deep Neural Networks for YouTube Recommendations”
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Wide and deep learning
https://research.googleblog.com/2016/06/wide-deep-learning-better-together-with.html
https://www.tensorflow.org/versions/r0.11/tutorials/wide_and_deep/index.html
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Multi-Criteria
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Graph-based algorithms
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Use of Semantic Web
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Thanks!