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Chang liu insight 2014
- 1. True Fit Skin Care Chang Liu Fellow at Insight Data Science 2014
- 2. So many products… What makes it so hard? Overwhelming information
- 3. What makes it so hard? Overwhelming information So many products… So many reviews…
- 4. What makes it so hard? Overwhelming information So many products… So many reviews… Reviews can be so long…
- 5. What makes it so hard? Overwhelming information So many products… So many reviews… Reviews can be so long… So many ingredients…
- 6. So many products… So many reviews… Time spent Money wasted Happiness What makes it so hard? Overwhelming information Reviews can be so long… So many ingredients…
- 7. 32k Reviewers • w/ 2+ reviews ~1200 Products • ~80 brands • 8 categories 184k Reviews • Rating [1-5] • Review text • Quick take Collaborative Filter using User Reviews from Sephora.com Product X Y … Reviewers 1 … 2 … 3 … … … … … N … Algorithm: • Item-centric collaborative filter • Pearson’s correlation coefficients to measure pairwise similarity
- 8. 32k Reviewers • w/ 2+ reviews ~1200 Products • ~80 brands • 8 categories 184k Reviews • Rating [1-5] • Review text • Quick take Collaborative Filter using User Reviews from Sephora.com Product X Y … Reviewers 1 … 2 … 3 … … … … … N … Algorithm: • Item-centric collaborative filter • Pearson’s correlation coefficients to measure pairwise similarity Similarity = cXY = (Xi - X)(Yi -Y ) N å i=1 N å (X- X)2 (Y-Y )2 i i i=1 N å i=1 M å / cij recommendation scoreui = rujcij j
- 9. 32k Reviewers • w/ 2+ reviews ~1200 Products • ~80 brands • 8 categories 184k Reviews • Rating [1-5] • Review text • Quick take Collaborative Filter using User Reviews from Sephora.com Product X Y … Reviewers 1 … 2 … 3 … … … … … N … Algorithm: • Item-centric collaborative filter • Pearson’s correlation coefficients to measure pairwise similarity Similarity = cXY = (Xi - X)(Yi -Y ) N å i=1 N å (X- X)2 (Y-Y )2 i i i=1 N å M å / cij Cross Validation • 5-fold for reviewer • Leave-one-out for product • Accuracy = 86.3% ± 1% i=1 recommendation scoreui = rujcij j
- 10. Visualize the similarity matrix White = high similarity Black = low similarity Sorted by brands alphabetically
- 11. White in a square = Users reviews are similar for all products in a brand = Strong customer loyalty There are structures!
- 12. Expensive! “Organic & Natural” There are structures! For example… Cost effective
- 13. There are structures! For example… Expensive! “Organic & Natural” Cost effective Actionable Insights For Sephora.com: Send marketing emails to new customers of brands with stronger customer loyalty!
- 14. Chang Liu PhD. in Civil Engineering @CMU J8D8L5@gmail.com linkedin.com/in/changliucmu github.com/R4trtry
- 15. Is the rating a good measure of reviewers’ perspective? • Trained a NaïveBaysian classifier for sentiment analysis • W/ 250 thousand reviews from Birchbox.com • A website that sends out free samples from smaller brands and gathers massive user reviews Most common words Most informative feature Word Count Negative Positive skin 91349 re-wash Penny product 82481 garbage hook use 64044 mediocre gorgeous love 55691 ketchup perk feel 47879 trash stock face 42615 unimpressive glowing like 41427 survey splurge great 34155 ineffective effortless really 31672 gag Christmas smell 27621 worthless happily text quick take Precision 95.3% 85.4% Recall 89.8% 93.1% Worth every penny! Another Validation
- 16. Another Validation Is the rating a good measure of reviewers’ perspective?
- 17. Product X Algorithm: Item-centric collaborative filter similarity 87.4% Product Y Product X Product Y 1 1 1 1 1 1 1 1 1 1 1 1 Reviewers Product X Y … 1 … 2 … 3 … … … … … N … M products reviewed by N reviewers Pairwise similarities are measured by Pearson's correlation coefficients: cXY = (Xi - X)(Yi -Y ) N å i=1 N å (X- X)2 (Y-Y )2 i i i=1 N å i=1 Then weight the ratings based on the correlation coefficients: Scorei = cijr uj M å j | cij | ruj : User u's preference on item j
Notes de l'éditeur
- Hi My name is Chang. I created true fit skin care, a web-app that recommend skin care products for you. I’m not an expert As you can see, the background is crowded with skin care products in boxes, bottles and jars. This is what it looks like in out bathroom. My wife
- Estee lauder, is not doing so well, it’s a bit expensive, so there are actually very small number of reviews per product. FAB, instead, is very cost effective, therefore has pretty good customer loyalty. Origins, on the other hand, makes products with organic and natural ingredients. Therefore, customer who likes their product are paying for this natural concept.
- Estee lauder, is not doing so well, it’s a bit expensive, so there are actually very small number of reviews per product. FAB, instead, is very cost effective, therefore has pretty good customer loyalty. Origins, on the other hand, makes products with organic and natural ingredients. Therefore, customer who likes their product are paying for this natural concept.
- And this is me, just finiwww.linkedin.com/in/changliucmu/shed phd in civil engineering at carnegie Mellon University. I studied pipe monitoring using data driven approach. The image here shows the transmission pipe lines across the US.