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Flexible recommender systems based on graphs


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Talk given by Kernix during the RecsysFR meetup on March 22nd, 2016.

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Flexible recommender systems based on graphs

  1. 1. | kernixdigital factory + data lab Flexible recommender systems based on graphs
  2. 2. | KERNIX 45 co-workers 500projects 2co-founders 3,5M€ revenue 15years experience 10books published Digital factory Data lab CO-FOUNDERS Fabrice Métayer and François-Xavier Bois, two EPITA engineers, gathered their complementary profiles to create Kernix in 2001. ABOUT KERNIX Kernix’s core business consists in a digital factory and a data lab. This double skill allows us to accompany our clients from upstream phases (consulting, study, POC) to downstream phases (industrialization by production teams).
  3. 3. | 3 DATA LAB Clients Collaborations EXPERTISE Data Pipelines Cop21 TerraRush Predictive maintenance ERDF Data Vizualisation SolarImpulse Recommender systems PriceMinister WikiDistrict Clickalto HobbyStreet Marketing Automation Performics RadiumOne Open Data
  4. 4. | • Graph database – data stored as nodes • label : “type” of data stored in the node • properties : collection of information describing the node – nodes are linked together by edges • type : describes the nature of the relation – query language : allows to perform graph traversals • Why graph-oriented recommender systems ? – gather heterogeneous data in the same structure – explicitly take advantage of relationships – "meaningful" for humans – easy implementation – fast execution (no training) GRAPH-ORIENTED RECOMMENDER SYSTEM
  6. 6. | Facilitate connections between craftsmen and private individuals • Craftsmen : propose workshops (different categories, dates, prices) • Individuals : follow workshops/categories, sign up at workshops • Hobbystreet : handle registrations, plannings, payments, propose customized suggestions CONTEXT
  7. 7. | DATA STRUCTURE User name city Carftman name activity Workshop name description GPS coordinates Session date, time price status stock Category name activity follows proposes related to instance of participates
  8. 8. | SUGGESTIONS : OVERALL STRATEGY Category User Workshop 1 Category 1 Category 2 Workshop 2 Workshop 3 Workshop 4 Similar descriptions User Workshop 1 Workshop 2 Workshop 3 Workshop 4 Workshop 5 Workshop 6 from LSA Similar users User 1 Workshop 1 Workshop 2 Workshop 3 User 2 User 3 Workshop 4 Workshop 5 Workshop 6 Usim
  9. 9. | USE CASE 2 : KONBINI
  10. 10. | Context “... multi format media company producing its own mix of culture, art and news content. It promotes online journalism, advocating an emphasis on pop culture and a commitment to develop local emerging talents.” “... became one of the first websites to put Social Media platforms at the heart of their strategy.”Issue: ~90% bounce rate (users going back after viewing a page) Solution: Recommend interesting articles on the visited pages will help user experience.
  11. 11. | Entities French posts [693] Authors [56] Categories [534] Mexican posts [149] English posts [417] Examples of node properties blog_id: 9 post_id: 217628 post_date: 20151007 slug: rihanna-thinks-rachel... boost: 0 viewed_count: 0 facebook_count: 148 twitter_count: 0 Multiple web sites [US, England, Mexic, France] US posts [364]
  12. 12. | Recommendations principles For each posts, we will recommend a list of other posts based on relations shared with the initial post: - semantic similarity of the contents [LSA] - number of common categories - number of common authors And also on their own properties: - the freshness - social counts - manual boost Once the graph constructed, these recommendations can be obtained thanks to a single Cypher query.
  13. 13. | Conclusion and outlook
  14. 14. | Stacks and Workflows Konbini web siteHobbystreet web site POST content GET recommendations POST content Daily cached recommendadions GET recommendations Live recommendation for dynamic interactions Cached recommendation for high availability needs
  15. 15. | Improve semantic analysis: • exploit similarity of short descriptions (tweets, comments, …). PhD thesis on the subject. Assess recommendation quality: • A/B testing but Needs production deployment. • Offline testing ? No real assessment on the impact of the recommendations performed. • Rating of pool of testers ? Outlook
  16. 16. THANK YOU ! Kernix Data Lab +33 (0)1 53 98 73 43