recommender systems in e-commerce. mlmu kosice 2017, exponea
TRANSCRIPT
Recommender systems in e-commerce
October2017
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Why recommendations?
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● Revenue
● Click-through rate
● Time spent
● User satisfaction
● Product/customer life-time value
Recommend to maximize objective
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General techniques
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Recommendation types
● New items on stock● Top selling/clicked items● Customer recent interaction● Collaborative filtering (CF)
○ Neighbourhood models○ Latent factor models
● Content based● Hybrid
No model
Model
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CF - Implicit vs Explicit feedback
● Explicit - wine ratings● Implicit - Purchases/views/add_to_cart …
○ http://yifanhu.net/PUB/cf.pdf
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CF - Neighbourhood methods
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CF - Latent factor models
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Content based
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Which one to use?
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● Revenue
● Click-through rate
● Time spent
● User satisfaction
● Product/customer life-time value
Recommend to maximize objective
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WRITE HERE SOMETHING● Revenue
● Click-through rate
● Product/customer life-time
value
● User satisfaction
● ...
Business
● MAP
● DCG/nDCG
● RMSE
● ...
Academia
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Are they correctly used?
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A/B test
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Can we do better?
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Multi-armed Bandit
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Contextual Bandit
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Where academia meet business
Offline A/B test
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Plenty of insights
● Hyper-parameter search● Combinations of recommenders● Lot of data to generalize from
○ Best homepage options○ Best product view○ Best cart recommender (if any)
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Future directions
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Where to look next?
● Better hybrid models
● Better individual models
● Contextual bandit algorithms
● Learning 2 rank
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Learning 2 rank
● Pointwise
○ Logistic regression, SVM, GBTs, ...
● Pairwise
○ RankSVM, RankNet, ...
● Listwise
○ ListNet, ESRank, ...
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