Key Lessons Learned Building Recommender Systems For Large-scale Social Networks

Transcription

1 Key Lessons Learned Building Recommender Systems For Large-scale Social Networks 1

2 The world s largest professional network Over 50% of members are now international 2/sec 165M+ * New members * 34th 90 Most visit website worldwide (Comscore) * 55 >2M Company pages ** LinkedIn Members (Millions) 75% Fortune 500 Companies use LinkedIn to hire ** *as of Nov 4, 2011 **as of June 30,

3 LinkedIn Homepage Powered by Recommendations! 3

4 LinkedIn Recruiting Solutions 4

5 The Recommendations Opportunity Similar Profiles Connections Network updates Events You May Be Interested In News 5

6 50% 6

7 Outline Some Fundamentals of RecSys Recommendation Lifecycle Right Time Recommendations at Scale 7

8 What is a Recommender System? A Recommender selects a product that if acquired by the buyer maximizes value of both buyer and seller at a given point in time Must make a strategic sense! 8

9 Ingredients of a Recommender System A Recommender processes information and transforms it into actionable knowledge Data Algorithms Algorithms Business Logic and Analytics User Experience??% 25%??% 5%??% 20%??% 50% Strands, RecSys 09

10 User Experience Matters 1. Understand user intent + interest à Define, model, leverage I-2 2. Be in the user flow 3. Location, location, location 4. Set right expectations ( You May. ) 5. Explain recommendations 6. Interact with the user 10

11 User Intent, User Flow, Location, Message Product Intent and flow Impact Jobs browsemaps Jobs You May be Interested In Jobs You May be Interested In Job description page: job seeker After having applied to a job: job applicant Homepage: all Job homepage: job seeker Job homepage center Job homepage right rail 7X application rate 10X application rate 5X application rate Item based collaborative filtering: à Followers Content based: à Leaders 11

12 User Intent Modeling 1. Intent labeling Job seeker, recruiter, outbound professional, sales, content consumer, content creator, networker, brander 2. Build normalized propensity model for each intent 3. Job Seeker: Active, receptive, non-seeking Score = Ordered Probit(behavior, content, and network) 10X application rate between active and passive receptive 12

13 Lifecycle of a Recommendation 1. Define clear objective à Metric to optimize 2. Build, train and evaluate new model Generate labeled and feature data 3. A/B test (and sometimes optimize) online 4. Rollout to all users and track if successful 5. Go back to 2! 13

14 Recommendation Objectives Beyond CTR Suggest relevant groups that one is more likely to participate in Suggest skilled candidates who will likely respond to hiring managers inquiries 14

15 MOO Multiple Objective Optimization Pareto-based multi-objective machine learning framework with two-layer scoring approach: 1. Optimize for relevance only 2. Then optimize for secondary objective while maintaining relevance à TalentMatch+Flightmeter = 55% lift in inmail reponse rate 15

16 Propensity Score The Group Case Social learning: people are followers People learn new behavior through observational learning of the social factors in their environment. If people observe positive, desired outcomes in the observed behavior, then they are more likely to model, imitate, and adopt the behavior themselves. Facebook Study 09 Newcomers may not recognize the value of contribution. [...] We find support for social learning: newcomers who see their friends contributing go on to share more content themselves. Burke et al: Feed Me: Motivating Newcomer Contribution in Social Network Sites, CHI 09 16

17 Propensity Score The Group Case One idea borrowed from expertise networks in online communities Group Activity = Directed Graph(V,A) V = group members i j if j comments on i s initial posting Zhang et al., Expertise Networks in Online Communities: Structure and Algorithms, WWW 07 17

18 Propensity Score The Group Case Indegree: Prestige Engaging more is more prestigious Outdegree: Influence Postings generating engagement Both follow a Power Law distributions P(d in ) ~ d in -α P(d out ) ~ d out -β è Group propensity score = F(α,β) 18

19 Platform Scaling innovation Cross-leverage improvements between products Shared knowledgebase Maintainability Production serving and tracking Infrastructure for complete upgrades Performance Billions of sub second computations 19

20 LinkedIn Recommendation Engine Recommendation Entities Products TalentMatch People Browse Map People Similar Profiles Referral Center Jobs You May be interested in Jobs Jobs Browse Map Similar Jobs GYML Groups Groups Browse Map Similar Groups Ads Companies Searches News Events and more to come API Recommendation Types Shared, Dynamic, Unified Core Service Content & Behavior Analysis Collaborative Filtering (R-T) Feature Extraction, Entity Resolution & Enrichment Popularity User Feedback (R-T) matching computations

21 Quick Overview of the Recommendation Process Input Entities Jobs, people, ads, news,, events, Target Entities People, jobs, groups,, Extracted Features Extracted Features Matching Matching Filtering Business Rules

22 Title Specialty Education Experience Location Industry Title Specialty Education Experience Location Industry Seniority Skills Related Titles Related Companies Related Industries Seniority Skills Related Titles Related Companies Related Industries Matching 0.58 Binary Exact match Exact match in bucket Soft Match v1 = tf * idf CosΘ = v1*v2 v1 * v2 Specialty à Specialty Skills à Skills 0.94 Title à Title 0.26 Seniority à Seniority 0.18 Summary à Summary 0.98 Title à Related Title 0.16 Education à Education 0.40.

23 Importance weight vector (Skills-> Skills) Similarity score vector (Skills-> Skills) Normalization, Scoring & Ranking Filtering Location Company Industry Feedback

24 Technologies

25 Data Sources for Feature Engineering Social Graphs Content Behavior PVs Queries Actions (clicks) 25

26 Feature Engineering Entity Resolution Companies IBM has variations - ibm ireland - ibm research - T J Watson Labs - International Bus. Machines K-Ambiguous Huge impact on the business and UE Ad targeting TalentMatch Referrals 26

27 Feature Engineering - Enrichment Zip code mapped to Regions How sticky are those locations? Huge impact on the business and UE Job Seeker, Recruiter

28 Feature Engineering - Enrichment How sticky are those locations? Region similarity based on profiles or network Region transition probability à predict individuals propensity to migrate and most likely migration target Impact on job recommendations 20% lift in views/viewers/applications/applicants

29 Feature Engineering - Enrichment Time-Based Seniority (Inferred) Skills User intent/need Job seeker (active, passive, not) Related entities (title, company, industry) Virtual Profiles for communities 29

30 Data Labeling Historical data Similar objective Unrelated processes, e.g., same session search selection reduce presentation bias, position bias What about intent bias? Random suggestions Great with ads, company follows Not for products with high cost of bad recommendations jobs, alumni groups, Not for similar recommendations Crowdsourced manual labeling Very challenging Pairwise comparison more suited than absolute rating à higher costs Expertsourced manual labeling 30

31 A/B Testing Is Option A Better Than Option B? Let s Test Beware of - novelty effect - cannibalization - potential biases (time, targeted population) - random sampling destroying the network effect Enjoy testing furiously! Don t forget to A/A test first,$!!!" +$!!!" *$!!!" )$!!!"!"#$%&'()$*'+$,-$#./0'1$+234'$5$67,788$ *$!!!" )$!!!" ($!!!"!"#$%&'()$*+,-+,,$ ($!!!" '$!!!" &$!!!" %$!!!" #$!!!"!"!" (" #!" #(" %!" %(" '$!!!" -./"01234"526"(7"/89:2;"6<=>2"?" )@(@##" &$!!!" %$!!!" #$!!!"!"!" (" #!" #(" %!" %(" +,-"./012")3#43##" ( Seven Pitfalls to Avoid when Running Controlled Experiments on the Web, KDD 09 Framework and Algorithms for Network Bucket Testing WWW 12 submission) 31