introduction and new trends in recommender systems

Introduction and new trends in Recommender Systems

Paolo Tomeo@paotomeo

Information overload

@mkapor

www.smartinsights.com/internet-marketing-statistics/happens-online-60-seconds

www.smartinsights.com/internet-marketing-statistics/happens-online-60-seconds

Recommender Systems“Software tools and techniques that provide suggestions for items that are most likely of interest to a particular user”

User – Item Interaction

Rating

Like

Click

Visualization

Search query

Ratings

Diversity matters

Suggerisce all'utente item simili a quelli che ha apprezzato in passato

Approaches

Content Based filtering

Collaborative filtering

Hybrid approaches

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

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

Suggerisce item apprezzati da altri utenti che hanno preferenze simili

Matrix factorization CF

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, …)

Beyond accuracyDiversity

Novelty

Serendipity

Explanation

Trust

Performance

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

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

(Some) New trends

Deep learning

Wide and deep learning

Multi-criteria

Graph-based algorithms

Use of Semantic Web

Deep learning

P. Covington, J. Adams, E. Sargin. “Deep Neural Networks for YouTube Recommendations”

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

Multi-Criteria

Graph-based algorithms

Use of Semantic Web

Thanks!