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Project Progress Report - Recommender Systems for Social Networks

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Progress Report Presentation www.srcf.ucam.org/~ahh29 Recommender Systems for Social Networks Amir H. Hajizamani twitter.com/amirhhz
A Recommender System? • Input: social graph – Users = nodes – Follows = directed edges • Output: Predictions – Edges expected to appear in future • Challenge: Big, Dynamic Dataset • Framework ... Obtain Scrub Explore Model Interpret http://www.dataists.com/2010/09/a-taxonomy-of-data-science/
Data: Obtaining and Scrubbing • API – JSON response, paging, rate limits • Python wrapper – Proxies • Crawler – Depth-first search – Maintain state with • Storage – JSON  • Scrub until consistent
Recommending (Exploring, Modelling & Predicting) • Basic stats – Power law relationships • Model – Social network  homophily • Recommendations – Ranked social similarity – Simple metric: Jaccard index
Any good? • Recommendations on test data – Original data with hidden edges • (later to use temporal snapshots) • Recall and Precision rates – High recall, low precision • Still work to do! – e.g. Use interaction graph

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Project Progress Report - Recommender Systems for Social Networks

  • 1. Progress Report Presentation www.srcf.ucam.org/~ahh29 Recommender Systems for Social Networks Amir H. Hajizamani twitter.com/amirhhz
  • 2. A Recommender System? • Input: social graph – Users = nodes – Follows = directed edges • Output: Predictions – Edges expected to appear in future • Challenge: Big, Dynamic Dataset • Framework ... Obtain Scrub Explore Model Interpret http://www.dataists.com/2010/09/a-taxonomy-of-data-science/
  • 3. Data: Obtaining and Scrubbing • API – JSON response, paging, rate limits • Python wrapper – Proxies • Crawler – Depth-first search – Maintain state with • Storage – JSON  • Scrub until consistent
  • 4. Recommending (Exploring, Modelling & Predicting) • Basic stats – Power law relationships • Model – Social network  homophily • Recommendations – Ranked social similarity – Simple metric: Jaccard index
  • 5. Any good? • Recommendations on test data – Original data with hidden edges • (later to use temporal snapshots) • Recall and Precision rates – High recall, low precision • Still work to do! – e.g. Use interaction graph