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Debdoot Mukherjee
Debdoot Mukherjee

This document discusses personalization and customer insights for fashion recommendations. It covers modeling customer profiles based on browsing history and preferences to provide personalized recommendations, notifications, and targeted offers. It also describes challenges for fashion recommendations compared to other domains like books and movies, and approaches for collaborative and content-based filtering recommendations.

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Understand your customers deeply ... Engage with a personal touch! #ItsPersonal Debdoot Mukherjee Personalization & Customer Insights @
Modeling Personalized Customer Engagement Data Driven Retail Functions Product Listings Handpicked For Me Notifications Fashion Feed Offers & Promotions Re-targeting Marketing Research Campaign Targeting Audience Monetization Category Planning Brand Benchmarking Content Design
Ephemeral and non-identifiable items unlike Books and Movies Extremely sparse user/item matrix Bias of products with higher inventory Exploration versus Exploitation trade-off Diversity and Serendipity Closest domain: News Articles Fashion What s diffe e t?
Recommend based on user profiles stored as preference / weight vectors on item features, learnt from relevance feedback on items. Good vector representation for items? Bag of product attributes does not work! Too many features, s a e o se histo y fo a si gle use , so p ofiles do t generalize. Learning preferences along latent factor / topical dimensions or product groups (clusters) helps. Maintain two user profiles: long term (taste), short term (intent) Incorporate time decay into browse history Degree of personalization depend on the amount of browse data BUT, att i ute ele a e does t suffi e. The e is so ethi g e a t aptu e ia att i utes a out so e fashio ite s that make them popular, others not. May be aesthetics. Feature / Content based Approach
Simple user-user, item-ite CF te h i ues do t o k ell e ause of extremely sparse user-item matrix Matrix factorization: Regularization is tricky and severe cold start. In practice, models are trained specific to each category of product, so maintaining separate models for cold start and warm start becomes difficult. Recent advances in Feature based Matrix Factorization address this - SVDFeature, Factorization Machines, RLFM, FOBM, fLDA, UFSM Train model on snapshot of active products for recommendations Collaborative Filtering Approaches
A good vector representation for items would make si ila ite s eigh o s i the e to spa e. #di e sio s should be not very high. Co-browse of items in a session is (weakly) indicative of si ila ity . u h a sig al ei fo ed a oss a y sessio s becomes strong. Inferring substitutable and complementary products Leskovic et al. KDD15 Train a logistic regressor with features defined on the similarity of item vectors represented as topics to predict whether two products are similar. LDA using the analogy (Item Document, Item Attribute Word) Core Idea: Joint training of logistic regressor and item topics by simultaneously optimizing both topic distributions and logistic parameters to maximize the joint likelihood of topic memberships and product similarity. Vector Representation for Items
We use this analogy so that existing models for finding representations in text / IR become applicable: Browse Session Document, Items Clicked Sentences, Item Attributes Words Evaluate LDA, Word2Vec, GloVe Yields varying levels of topical and functional similarities along dimensions of the item vector ea h fo si ila te s fo nike : Topical Similarity: adidas, puma, sports, dry-fit, polyeste spo ts elated te s Functional Similarity: adidas, puma, fila, merrell, hrx Mining interesting relationships between entities of interest viz. brand, price band, pattern, item collection etc. Spherical clustering to create product groups a better unit of analyses than individual products. Create user profiles by aggregating their preferences on such item dimensions and product groups across all browsing sessions. Vector Representation for Items (2)
Explore/Exploit trade off Popularity scoring of items (normalized for each category / product group) Use Thomson Sampling in a context free bandit formulation that assumes Gaussian reward (CTR) Adjust CTR with rank to formulate reward Contextual bandits can help in choosing the right recommendation strategy given page and session context Use of LSH to ensure diversity of recommendations Explore / Exploit, Diversity ..
Personalized Product Listings Devarshi A Football Enthusiast Anand Appreciates Value for Money
Personalized Product Listings Devarshi A Football Enthusiast Anand Appreciates Value for Money
Handpicked For Me Personalized page with different kinds of recommendations: Taste, Intent based Cross sell based on last purchase
Notifications Drive a contextual conversation y i i g the use s shoppi g intent in real time
Customer Insights Platform A platform to slice / dice mined customer profiles has over 50K different dimensions
Create a segment of loyal customers in Delhi who wear heels Affinity toward heels Highly loyal From Delhi
Delhi women have a greater affinity for taller heel heights than Chennai women A woman from Delhi is 2x more likely to be interested in stilettos than someone from Chennai
Brand A Brand CBrand B 48.5% - 26 yr 27% - 28 yr 15% - 26 yr 1.8%-29y Loyalist Distribution 0.4% - 28 yr Comparing 3 Men Shirt Brands and their loyalists Compared to an average Myntra customer What else do the loyalists buy? Less likely More likely
Personalization ServicesMyntra.com Event Distributions Event Processor S3 Event Storage Cassandra (Clickstream Aggregates) Model Training Serving Caches Near-line Personalization Online Personalization Customer Profile ETL Customer- wise Event Aggregator Mongo (Customer Insights) Insights Platform Architecture Transactional DW Product Knowledge Graph
Ariana Grande M perso al st le is a i ture of, like, girl , throwback, like retro '50s pin-ups, floral, like hippies, like a thi g fe i i e, a d like flirt . Perso al t le is about having a sense of yourself what you believe i ever da Ralph Lauren Ever o e looks at your watch and it represents who you are, your values and your personal st le Kobe Bryant And You Still Think I Would Know About Personal Style ?!! Read more at http://sartorialscience.myntrablogs.com

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meetup-talk

  • 1. Understand your customers deeply ... Engage with a personal touch! #ItsPersonal Debdoot Mukherjee Personalization & Customer Insights @
  • 2. Modeling Personalized Customer Engagement Data Driven Retail Functions Product Listings Handpicked For Me Notifications Fashion Feed Offers & Promotions Re-targeting Marketing Research Campaign Targeting Audience Monetization Category Planning Brand Benchmarking Content Design
  • 3. Ephemeral and non-identifiable items unlike Books and Movies Extremely sparse user/item matrix Bias of products with higher inventory Exploration versus Exploitation trade-off Diversity and Serendipity Closest domain: News Articles Fashion What s diffe e t?
  • 4. Recommend based on user profiles stored as preference / weight vectors on item features, learnt from relevance feedback on items. Good vector representation for items? Bag of product attributes does not work! Too many features, s a e o se histo y fo a si gle use , so p ofiles do t generalize. Learning preferences along latent factor / topical dimensions or product groups (clusters) helps. Maintain two user profiles: long term (taste), short term (intent) Incorporate time decay into browse history Degree of personalization depend on the amount of browse data BUT, att i ute ele a e does t suffi e. The e is so ethi g e a t aptu e ia att i utes a out so e fashio ite s that make them popular, others not. May be aesthetics. Feature / Content based Approach
  • 5. Simple user-user, item-ite CF te h i ues do t o k ell e ause of extremely sparse user-item matrix Matrix factorization: Regularization is tricky and severe cold start. In practice, models are trained specific to each category of product, so maintaining separate models for cold start and warm start becomes difficult. Recent advances in Feature based Matrix Factorization address this - SVDFeature, Factorization Machines, RLFM, FOBM, fLDA, UFSM Train model on snapshot of active products for recommendations Collaborative Filtering Approaches
  • 6. A good vector representation for items would make si ila ite s eigh o s i the e to spa e. #di e sio s should be not very high. Co-browse of items in a session is (weakly) indicative of si ila ity . u h a sig al ei fo ed a oss a y sessio s becomes strong. Inferring substitutable and complementary products Leskovic et al. KDD15 Train a logistic regressor with features defined on the similarity of item vectors represented as topics to predict whether two products are similar. LDA using the analogy (Item Document, Item Attribute Word) Core Idea: Joint training of logistic regressor and item topics by simultaneously optimizing both topic distributions and logistic parameters to maximize the joint likelihood of topic memberships and product similarity. Vector Representation for Items
  • 7. We use this analogy so that existing models for finding representations in text / IR become applicable: Browse Session Document, Items Clicked Sentences, Item Attributes Words Evaluate LDA, Word2Vec, GloVe Yields varying levels of topical and functional similarities along dimensions of the item vector ea h fo si ila te s fo nike : Topical Similarity: adidas, puma, sports, dry-fit, polyeste spo ts elated te s Functional Similarity: adidas, puma, fila, merrell, hrx Mining interesting relationships between entities of interest viz. brand, price band, pattern, item collection etc. Spherical clustering to create product groups a better unit of analyses than individual products. Create user profiles by aggregating their preferences on such item dimensions and product groups across all browsing sessions. Vector Representation for Items (2)
  • 8. Explore/Exploit trade off Popularity scoring of items (normalized for each category / product group) Use Thomson Sampling in a context free bandit formulation that assumes Gaussian reward (CTR) Adjust CTR with rank to formulate reward Contextual bandits can help in choosing the right recommendation strategy given page and session context Use of LSH to ensure diversity of recommendations Explore / Exploit, Diversity ..
  • 9. Personalized Product Listings Devarshi A Football Enthusiast Anand Appreciates Value for Money
  • 10. Personalized Product Listings Devarshi A Football Enthusiast Anand Appreciates Value for Money
  • 11. Handpicked For Me Personalized page with different kinds of recommendations: Taste, Intent based Cross sell based on last purchase
  • 12. Notifications Drive a contextual conversation y i i g the use s shoppi g intent in real time
  • 13. Customer Insights Platform A platform to slice / dice mined customer profiles has over 50K different dimensions
  • 14. Create a segment of loyal customers in Delhi who wear heels Affinity toward heels Highly loyal From Delhi
  • 15. Delhi women have a greater affinity for taller heel heights than Chennai women A woman from Delhi is 2x more likely to be interested in stilettos than someone from Chennai
  • 16. Brand A Brand CBrand B 48.5% - 26 yr 27% - 28 yr 15% - 26 yr 1.8%-29y Loyalist Distribution 0.4% - 28 yr Comparing 3 Men Shirt Brands and their loyalists Compared to an average Myntra customer What else do the loyalists buy? Less likely More likely
  • 17. Personalization ServicesMyntra.com Event Distributions Event Processor S3 Event Storage Cassandra (Clickstream Aggregates) Model Training Serving Caches Near-line Personalization Online Personalization Customer Profile ETL Customer- wise Event Aggregator Mongo (Customer Insights) Insights Platform Architecture Transactional DW Product Knowledge Graph
  • 18. Ariana Grande M perso al st le is a i ture of, like, girl , throwback, like retro '50s pin-ups, floral, like hippies, like a thi g fe i i e, a d like flirt . Perso al t le is about having a sense of yourself what you believe i ever da Ralph Lauren Ever o e looks at your watch and it represents who you are, your values and your personal st le Kobe Bryant And You Still Think I Would Know About Personal Style ?!! Read more at http://sartorialscience.myntrablogs.com