An Online Social-Based Recommendations System

1. An Online Social-Based Recommendations System Danny Tarlow, Jeremy Handcock, Inmar Givoni, and Jorge Aranda CSC2231, December 2007

2. Online recommendations • Same author, same genre • “Customers that bought this also bought…” • Move towards more sophisticated algorithms • Netflix challenge • Attempt to use more information for the recommendations Successful, but with room for improvement

3. Intuition of our approach • Homophily • We tend to get together with people that are like us • We often have similar preferences • New information available online • What people like • How they are connected socially • Idea • Link current machine learning recommendations technology with the social information now available

4. Problem and goals Goals Find out how to take advantage of social information for recommendation algorithms Build an application that implements our approach Test whether social data improve recommendations Our application… Pulls preferences and social ties out of a community website Gives recommendations to users based on their preferences and ties Useful for recommendations in many current online social applications 4

5. Our subject We needed a website with publicly available information on preferences and social ties Boardgamegeek.com is the largest and most popular online community for boardgames and cardgames enthusiasts • >32K games • >42K users, of which 30K have rated games • 1.3 million ratings • >128K social (GeekBuddy) ties 5

6. Recommendations algorithm PMF – Probabilistic Matrix Factorization Idea: People are not that complex We can use the combination of a few descriptors for any of us (e.g., a strategy gamer who likes wacky themes) We also see how much each game fits to each descriptor We predict a user will like a game if it fits the descriptors of which he is “made of” Our algorithm finds these descriptors automatically Social information becomes part of our description for each user 6

7. Results and Application Using social information improves the performance of the PMF learning algorithm We plugged in the algorithm to our web application 7

8. Conclusion Contribution: We developed an algorithm that takes into account social information Our algorithm increases the prediction accuracy We created a website to allow the gaming community to get better recommendations Open questions: Incorporating preferences and social information from different websites? Identity management Does homophily really play a role here? 8