Download - Temporal Diversity in Recommender Systems
Temporal Diversity in Recommender SystemsNeal Lathia, Stephen Hailes, Licia Capra, and Xavier AmatriainSIGIR 2010
April 6, 2011Hyunwoo Kim
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Outline Introduction Why Temporal Diversity? Evaluating for Diversity Promoting Temporal Diversity Conclusion
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Introduction Collaborative Filtering [Kim, ECRA2010]
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Introduction
in 2006
in 2011Alice
User’s interest changes over time [Zheng, ESWC2011]
baby health
education
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Introduction A problem with current evaluation techniques
– No temporal characteristics of the produced recommen-dations
In this work,– Diversity of top-N lists over time
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Why Temporal Diversity? Two perspectives
– Changes that CF data undergoes over time– How surveyed users respond to recommendations with vary-
ing levels of diversity
Changes over time– Continuous rating of content– Recommender systems have to make decisions based on
INCOMPLETE and CHANGING data– A list at any particular time is likely to be different with pre-
vious list
– Do these changes translate into different recommendations over time?
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Why Temporal Diversity? User survey
– Popular movies from
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Why Temporal Diversity? User survey
– S1: popular movies with no diversity– S2: popular movies with diversity– S3: randomly selected movies
In S3, some users com-mented:
“appeared to very random”“varied widely”
“avoided box office hits”…
In S1, some users com-mented:
“lack of diversity persisted”“too naïve”
“not working”“decreased interest”
…Users are responding to the im-pression of the recommender system!!
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Why Temporal Diversity? Qualities in recommendations
– ACCURATE recommendations– CHANGE OVER TIME– NEW recommendations
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Evaluating for Diversity How diverse CF algorithms are over time
– Baseline: item’s mean rating– Item-based k-Nearest Neighbor (kNN)– Matrix factorization approach based on Singular Value De-
composition (SVD)
Dataset– Netflix prize dataset
To improve the accuracy of predictions about how much some-one is going to enjoy a movie based on their movie preferences
$1,000,000 grand prize on September 21, 2009
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Evaluating for Diversity Diversity and novelty
Last week’s list
This week’s list
Diversity = 1/5
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Evaluating for Diversity Diversity and novelty
Previous recommen-dations
This week’s list
Novelty = 2/5
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Evaluating for Diversity Diversity results and analysis
– Baseline produces little to no diversity– Factorization and nearest neighbor approaches increment di-
versity
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Evaluating for Diversity Novelty results and analysis
– Novelty values are lower than diversity values– When different a recommendation appears, it is a recom-
mendation at some point in the past
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Evaluating for Diversity How diversity relates to accuracy
– RMSE: Root Mean Squared Error– Different algorithms often overlap and kNN CF is sometimes less
accurate than the baseline
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Promoting Temporal Diversity Diversity comes at the cost of accuracy When promoting diversity, we must continue to take
into account users’ preferences
Three methods– Temporal switching– Temporal user-based switching– Re-ranking frequent visitors’ lists
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Promoting Temporal Diversity Temporal switching
Temporal user-based switching
kNN SVD SVDkNN kNN
kNN SVD SVDkNN kNN
user login user login user login
1st 2nd 3rd 4th 5th
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Promoting Temporal Diversity Temporal switching from a system
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Promoting Temporal Diversity Temporal user-based switching
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Promoting Temporal Diversity Re-ranking frequent visitors’ lists
Full listTop-5 list Re-ranking list
Diversity 40%
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Promoting Temporal Diversity Re-ranking frequent visitors’ lists
– Only a single CF algorithm is used
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Conclusion What we found
– State-of-the-art CF algorithms produce low temporal diversity– They repeatedly recommend the same top-N items to users
What we did– A metric to measure temporal diversity– A fine-grained analysis of the factors that may influence di-
versity
Future work– How novel items find their way into recommendations– How user rating patterns can be used to improve recom-
mender system’s resilience to attack