Maarten de Rijke sprak op de eerste sessie van de vierdelige Master Minds on Data Science over personalization & content recommendation

1. Amsterdam Data Science brings together data
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Amsterdam
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Amsterdam Data Science
Maarten de Rijke
http://amsterdamdatascience.nl

2. Amsterdam
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Four knowledge institutes, 300+ researchers, application domains
business analytics, health and life science, creative industries,
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6. Amsterdam
DATA SCIENCE
Personalization & content
recommendation
Maarten de Rijke

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Background

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http://bakadesuyo.bakadesuyo.netdna-cdn.com/wp-content/uploads/2012/12/20110504181819_books.jpg

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http://archive.rusbase.com/media/blogs/apps.png

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http://i.huffpost.com/gen/1293353/images/o-TV-facebook.jpg

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https://beingjaffa.files.wordpress.com/2013/10/facebook-faces.jpg

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?

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Recommender systems
Ò For consumer
Ò Find things that are interesting
Ò Narrow down the set of choices
Ò Discover new things
Ò Entertainment
Ò …
Ò For producer
Ò Personalized service for the customer
Ò Increase trust and customer loyalty
Ò Increase sales, click trough rates, conversion etc.
Ò Opportunities for promotion, persuasion
Ò Obtain more knowledge about customers
Ò …

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https://en.wikipedia.org/wiki/Recommender_system

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Recommender systems
Ò Given
Ò a user, items
Ò return
Ò a ranked list of items (that are most interesting,
relevant, entertaining, useful, …)
Ò How is this different from a regular search
engine?

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The user is
the query

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So many criteria

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So many ways of ranking items that are
useful, interesting, relevant, …
Ò Collaborative filtering
Ò Build a model from a user's past behavior (items previously
purchased or selected and/or numerical ratings given to
those items) as well as similar decisions made by other
users. Use model to predict items (or ratings for items) that
the user may have an interest in
Ò Example
Ò Last.fm creates a “station” of recommended songs by
observing what bands and individual tracks user has
listened to on a regular basis and comparing those
against the listening behavior of other users
Ò Last.fm will play tracks that do not appear in user's library,
but are often played by other users with similar interests
Ò Cold start problem (new users, new items)

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So many ways of ranking items that are
useful, interesting, relevant, …
Ò Content-based: utilize a series of characteristics
of an item in order to recommend additional
items with similar properties…
Ò Pandora uses the properties of a song or artist (a
subset of the 400 attributes provided by the Music
Genome Project) in order to seed a “station” that plays
music with similar properties
Ò User feedback is used to refine the station's results,
deemphasizing certain attributes when a user “dislikes”
a particular song and emphasizing other attributes
when a user “likes” a song
Ò Needs some sort of content descriptors
Ò Cold start problem (new users)

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So many ways of ranking items that are
useful, interesting, relevant, …
Ò Knowledge-based: utilize a series of constraints
expressed by the user
Ò I only want black cars
Ò No cold-start problem

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So many ways of ranking items that are
useful, interesting, relevant, …
Ò Probabilistic methods
Ò Given user/item rating matrix, determine the probability
that user will like a new item (based on past
observations)
Ò Matrix factorization methods
Ò Principal component analysis
Ò Popularity
Ò Trends
Ò Deep learning
Ò …

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Learning to rank

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Recommendations are ranked lists
Ò We don’t care so much about the ratings as
about the order in which recommended results
are presented
Ò Automatically learn to rank
Ò Learn an individual ranking of items
Ò Learn to combine multiple rankings

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Recommendations are ranked lists
Ò Learning to rank
Ò Pointwise
Ò Learn a score per item
Ò Pairwise
Ò Learn pairwise classifications
Ò Listwise
Ò Directly optimize a whole result page

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Illustration: listwise learning

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Increasingly: learning from implicit
feedback

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Bandits
Ò In many domains items are constantly new (e.g., news
recommendation, computational advertisement)
Ò Contextual Bandits are on-line learning algorithms that work
by “exploring” the user preference space and “exploiting” the
resulting models in serving recommendations
Ò Active research in finding optimal strategies for ranking items

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Online experimentation

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Run experiments online, all of the time
Ò Train offline up to a certain performance level
Ò Take your recommendation system online
Ò Let it learn from mostly implicit feedback from its
users
Ò Two methods: A/B testing vs. interleaving

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Run experiments online, all of the time

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Run experiments online, all of the time

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Run experiments online, all of the time

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Run experiments online, all of the time

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Run experiments online, all of the time

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Responsible data science

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Pitfalls?

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Pitfalls?
Ò Questions about the outcomes of your online
experiments
Ò How can we achieve high classification accuracy while
eliminating discriminatory biases? What are meaningful
formal fairness properties?
Ò How can we design expressive yet easily interpretable
recommenders?
Ò Can we ensure that a recommender remains accurate
even if the statistical signal it relies on is exposed to
public scrutiny?
Ò Are there practical methods to test existing
recommenders for compliance with a policy?

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Pitfalls?
Ò Questions about the fact that your running online
experiments with real users
Ò Should we be doing this?
Ò Facebook emotion contagion study
Ò This is not about privacy

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Wrap-up

42. 80% of tablet and smartphone owners use
their device while watching TV.
(Nielsen, 2011; Razorfish, 2011; Google 2012)
Case & his friends watching Superbowl XLVII, by Scott K. Macklin (mcdm.uw.edu)

43. Content?
38% of mobile
multitaskers access
content that is related
to the TV program
(Razorfish, 2011; Nielsen, 2011)
80% of tablet and
smartphone owners use their
device while watching TV.
(Nielsen, 2011; Razorfish, 2011;
Google 2012)

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Invitation
Ò MediaNow project
Ò Narrative Search
Ò The search engine result page tells a story (instead of
giving 10 blue links)
Ò Looking for media professionals and their search
behavior

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How well will this work?
Ò Over 80% of Netflix consumptions comes from
recommendation
Ò Should we aim for 100%?

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The next frontierdigital immortality

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Research supported by