Bimodal invitation-navigation fair bets model for authority identification in a social network
Proceedings of the 21st international conference on World Wide Web
Serving large-scale batch computed data with project Voldemort
FAST'12 Proceedings of the 10th USENIX conference on File and Storage Technologies
Learning to rank social update streams
SIGIR '12 Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval
Multiple objective optimization in recommender systems
Proceedings of the sixth ACM conference on Recommender systems
Social referral: leveraging network connections to deliver recommendations
Proceedings of the sixth ACM conference on Recommender systems
Avatara: OLAP for web-scale analytics products
Proceedings of the VLDB Endowment
Metaphor: a system for related search recommendations
Proceedings of the 21st ACM international conference on Information and knowledge management
Organizational overlap on social networks and its applications
Proceedings of the 22nd international conference on World Wide Web
Is it time for a career switch?
Proceedings of the 22nd international conference on World Wide Web
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Online social networks have become very important for networking, communication, sharing, and content discovery. Recommender systems play a significant role on any online social network for engaging members, recruiting new members, and recommending other members to connect with. This talk presents challenges in recommender systems, graph analysis, social stream relevance and virality on a large-scale social networks such as LinkedIn, the largest professional network with more than 200M members. First, social recommender systems for recommending jobs, groups, companies to follow, other members to connect with, are very important part of a professional network like LinkedIn [1, 6, 7, 9]. Each one of these entity recommender systems present novel challenges to use social and member generated data. Second, various problems, such as, link prediction, visualizing connection network, finding the strength of each connection, and the best path among members, require large-scale social graph analysis, and present unique research opportunities [2, 5]. Third, social stream relevance and capturing virality in social products are crucial for engaging users on any online social network [4]. Final, systems challenges must be addressed in scaling recommender systems on a large-scale social networks [3, 8, 10]. This talk presents challenges and interesting problems in large-scale social recommender systems, and describes some of the solutions.