increase basket value by offering better recommendations - vesa hyppönen, frosmo at frosmox16
TRANSCRIPT
Beyond relevancy and accuracy in recommendations
FrosmoX16
Vesa-Matti Hyppönen 24.10.2016
This is Mekbib
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● This is Mekbib and his 2 year old son● He is sitting outside in a cafe● His shirt is blue● Mekbib could not make it here today
This is all accurate, but not very relevant in current context
This is Mekbib
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● This is Mekbib and his 2 year old son● He works at Frosmo in the customer
service function● I am his replacement as a speaker today● Mekbib could not be here because he
couldn’t not fit his hair into a passport picture
Accuracy without context and relevance could be misinformation
You also need Relevancy
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• Google search for “The lion king” = 13 600 000 results
• All these documents have many occurrences of the word “The”
• Less frequent occurrences of “Lion” & “king”
• Teaching the recommendation machine to find relevant data
• To offset the abundance of “The”, we lower the weight by checking its term
frequency and inverse document frequency
Tf-idf used to determine Relevancy
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Tf-idf is a weighting factor to show how important an item is related to the whole corpus
Why is Accuracy + Relevancy != Enough?
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• Recommendations can produce accurate and relevant info but be useless
• Because it fails the test of obviousness
• Because it fails to give the users any new info
• Wasting valuable screen estate, resources and money
• Factor in previous user interaction on site
We need to add the element of novelty & serendipity
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• Novelty = Recommending undiscovered items
• Serendipity = Measure of how the items are attractive and surprising to the
user
• Unexpectedness and usefulness
• Judged from subjective perspective
How to measure serendipity?
Collaborative filtering
- Memory based, easier, neighbourhood based
- Model based, more complex - non obvious
Neighborhood based automatic
collaborative filtering using pearson correlation
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Conclusion
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• Obvious recommendations
• Waste space by fail to introduce new items or giving users information
they already know
• Make user blind to website real estate
• Recommendations should rise beyond accuracy and relevancy
• They should be novel and serendipitous as well
• Technically challenging but will self adust user classification
• Don’t forget to A/B test - results might be surprising
