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Injecting semantic links into a graph-based recommender system

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Simon Lefebvre - Antvoice Talk given during the 6th RecSysFR meetup on June 27th 2017.

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Injecting Semantic links into a graph-based recommender system Février 2017
At the beginning there were Facebook Apps Will they match ? Will he buy ? The Mood Weather Report Buy icons to be displayed on your Facebook timeline BeMyGentleman Dating WebSite
The multi-object graph-based recommender T T Diet Beauty T Mortgage T Health TSport T White color Tag Content Product User Clicked Watched Viewed Read Bought Added to basket Collect data and keep it in its original graph structure - Any type of object can be used & recommended (tag, user, product, content … ) - Edges are build from tracked actions : buyings, readings, tagging…) Compute click predictions from the paths linking users to objects we could recommend
General workflow Graph processor : - Aggregates edges - Trims irrelevant ones Reduced Graph Complete Graph Learning Base Recommender - Suggests objects - Collects successes & failures ParametersMachine learning - Set rules for graph processing - Optimize real-time decisions
Use cases Sélectionné pour vous Sélectionné pour vous Recommendation : venues on TickeTac Edges : tracking on website only Recommendation : advertisment for Delamaison furnitures on Prisma Group Eges : readings on Femme Actuelle & tracking on website Recommendation : One site, advertisment Edges : tracking on website, content views
Using media content to find targets Read Bought Read Read Watched Watched LEA, ID 456 Lose weight before summer FemmeActuelle content Rome : the Eternal City Geo content Real Estate : interest rate Capital content TéléLoisir, Top Chef, cuisine, M6, télé réalité Would Lea be interested by l’Oréal ? - Problem : the campaign has never been launched : no natural « L’Oréal » node connected to the general graph First idea : use editorial tags - Low quality - Not exhaustive - Not standardized among publications Second idea : tag content with a semantic algorithm
Automatic tagging of media content 𝐴~𝑇𝑘 𝑆 𝑘 𝐷 𝑘 𝑇 The corpus is composed of media contents The thesaurus contains 40 000 expressions a marketing operator would be willing to search. 1) Corpus Processing : lematization, entropy computation 2) Truncated Singular value decomposition of the Document / Term matrix Document / Term matrix Term / Semantic space matrix Singular Values (we keep the 250 highest values) Document / Semantic space matrix 3) Projection of the thesaurus onto the semantic space 𝑑 𝑘~𝑎 𝑇 𝑇𝑘 𝑆 𝑘 −1 3) Tag attribution by cosinus computation
Connecting user to campaign User – Content : collected on the web Shortest path from user to campaign Content – Topic : semantic algorithm Topic – Product : chosen by the operator Can be very subjective ! Mosturizer ? Skin health ? Cosmetic ? Dry skin ?
Behavioral & Semantic extension Pure semantic extension : a lot of articles are tagged with both topics Semantic link User action Behavioral extension : a lot of users are interrested in both topics Mosturizer Hydration Mosturizer Food Supplements
And more cool projects … Recommendation Target Generator Digital Advertising Blouse col ruché Dernière tendance, la blouse se révèle pratique et chic [...) 25,90€ 18,20€ - 3O % Sélectionné pour vous sur Un produit identifié pour vous sur Mini Pipistrello Lampe noire Touch Led H35 cm [...) 729,00 € 495,00 0€ - 32 % VIDEOS – Jade et Arnaud Lagar- dère : avant « Envoyé Spécial », ils s'étaient illustrés dans des séquences inoubliables ARTICLES – Lola Dewaere, la fille de Patrick Dewaere, refuse de participer à DALS avec beaucoup d’humour et de [...] - Find users reading about topics linked to your product. - Choose topics among 40k possibilities. - Real-time personalized advertising - Target new high-potential users on a display network - Improve the user experience on your website. - Increase your conversion rate.
Join us ! We have money … 2,8 M€ raised More and more clients … We need talents ! To grow To lauch new products To internationalize simon@antvoice.com

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Injecting semantic links into a graph-based recommender system

  • 1. Injecting Semantic links into a graph-based recommender system Février 2017
  • 2. At the beginning there were Facebook Apps Will they match ? Will he buy ? The Mood Weather Report Buy icons to be displayed on your Facebook timeline BeMyGentleman Dating WebSite
  • 3. The multi-object graph-based recommender T T Diet Beauty T Mortgage T Health TSport T White color Tag Content Product User Clicked Watched Viewed Read Bought Added to basket Collect data and keep it in its original graph structure - Any type of object can be used & recommended (tag, user, product, content … ) - Edges are build from tracked actions : buyings, readings, tagging…) Compute click predictions from the paths linking users to objects we could recommend
  • 4. General workflow Graph processor : - Aggregates edges - Trims irrelevant ones Reduced Graph Complete Graph Learning Base Recommender - Suggests objects - Collects successes & failures ParametersMachine learning - Set rules for graph processing - Optimize real-time decisions
  • 5. Use cases Sélectionné pour vous Sélectionné pour vous Recommendation : venues on TickeTac Edges : tracking on website only Recommendation : advertisment for Delamaison furnitures on Prisma Group Eges : readings on Femme Actuelle & tracking on website Recommendation : One site, advertisment Edges : tracking on website, content views
  • 6. Using media content to find targets Read Bought Read Read Watched Watched LEA, ID 456 Lose weight before summer FemmeActuelle content Rome : the Eternal City Geo content Real Estate : interest rate Capital content TéléLoisir, Top Chef, cuisine, M6, télé réalité Would Lea be interested by l’Oréal ? - Problem : the campaign has never been launched : no natural « L’Oréal » node connected to the general graph First idea : use editorial tags - Low quality - Not exhaustive - Not standardized among publications Second idea : tag content with a semantic algorithm
  • 7. Automatic tagging of media content 𝐴~𝑇𝑘 𝑆 𝑘 𝐷 𝑘 𝑇 The corpus is composed of media contents The thesaurus contains 40 000 expressions a marketing operator would be willing to search. 1) Corpus Processing : lematization, entropy computation 2) Truncated Singular value decomposition of the Document / Term matrix Document / Term matrix Term / Semantic space matrix Singular Values (we keep the 250 highest values) Document / Semantic space matrix 3) Projection of the thesaurus onto the semantic space 𝑑 𝑘~𝑎 𝑇 𝑇𝑘 𝑆 𝑘 −1 3) Tag attribution by cosinus computation
  • 8. Connecting user to campaign User – Content : collected on the web Shortest path from user to campaign Content – Topic : semantic algorithm Topic – Product : chosen by the operator Can be very subjective ! Mosturizer ? Skin health ? Cosmetic ? Dry skin ?
  • 9. Behavioral & Semantic extension Pure semantic extension : a lot of articles are tagged with both topics Semantic link User action Behavioral extension : a lot of users are interrested in both topics Mosturizer Hydration Mosturizer Food Supplements
  • 10. And more cool projects … Recommendation Target Generator Digital Advertising Blouse col ruché Dernière tendance, la blouse se révèle pratique et chic [...) 25,90€ 18,20€ - 3O % Sélectionné pour vous sur Un produit identifié pour vous sur Mini Pipistrello Lampe noire Touch Led H35 cm [...) 729,00 € 495,00 0€ - 32 % VIDEOS – Jade et Arnaud Lagar- dère : avant « Envoyé Spécial », ils s'étaient illustrés dans des séquences inoubliables ARTICLES – Lola Dewaere, la fille de Patrick Dewaere, refuse de participer à DALS avec beaucoup d’humour et de [...] - Find users reading about topics linked to your product. - Choose topics among 40k possibilities. - Real-time personalized advertising - Target new high-potential users on a display network - Improve the user experience on your website. - Increase your conversion rate.
  • 11. Join us ! We have money … 2,8 M€ raised More and more clients … We need talents ! To grow To lauch new products To internationalize simon@antvoice.com