Large-scale social recommender systems: challenges and opportunities

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.