Group Recommendation in Social Networks.

摘要

Recent years have seen an exponential growth in the use of social networking systems, enabling their users to easily share information with their connections. A typical Facebook user, as an example, might have 300-400 connections that include relatives, friends, business associates and casual acquaintances. Sharing information with such a large and diverse set of people without violating social norms or privacy can be challenging. Allowing users to define groups and restrict information sharing by group reduces the problem but introduces new ones: managing groups and their members, relations and information sharing policies. This thesis addresses the problem of maintaining group membership. We describe a system that learns to classify a user's new connections into one or more existing groups based on the connection's attributes and relations. We demonstrate the approach using data collected from real Facebook users. The two major tasks are identifying the relevant features for the classification and selecting the learning mechanism that best suits the task. Hierarchical and overlapping groups pose another significant challenge. We show that our system classifies new connections into these groups with high accuracy even with only 10-20% of labeled data.