An Effective Friend Recommendation Method Using Learning to Rank and Social Influence

Social network sites have become an important medium for people to receive information anytime anywhere. Users of social network sites share information by posting updates. The updates shared by friends form social update streams that provide people with up-to-date information. To receive novel information, users of social network sites are encouraged to establish social relations. However, having too many friends can lead to an information overload problem causing users to be overwhelmed by the huge number of updates shared continuously by numerous friends. The information overload problem can result in bad user experiences. It may also affect user intentions to join social network sites and thereby possibly reduce the sites’ advertising earnings which are based on the number of users. To resolve this problem, there is an urgent need of effective friend recommendation methods. A user is considered as a valuable friend if people like the updates the user posts. In this paper, we propose a model-based recommendation method which suggests valuable friends to users. Techniques of matrix factorization and learning to rank are designed to model the latent preferences of users and updates. At the same time, social influence is incorporated into the proposed method to enhance the learned preferences. Valuable friends are recommended if the preferences of the updates that they share are highly associated with the preferences of a target user. Our experiment findings that are based on a huge real-world dataset demonstrate the effectiveness of the social influence and learning to rank on a friend recommendation task. The results show that the proposed method is effective and it outperforms many well-known friend recommendation methods in terms of the coverage rate and ranking performance

Network Analysis on Incomplete Structures.

Over the past decade, networks have become an increasingly popular abstraction for problems in the physical, life, social and information sciences. Network analysis can be used to extract insights into an underlying system from the structure of its network representation. One of the challenges of applying network analysis is the fact that networks do not always have an observed and complete structure. This dissertation focuses on the problem of imputation and/or inference in the presence of incomplete network structures. I propose four novel systems, each of which, contain a module that involves the inference or imputation of an incomplete network that is necessary to complete the end task. I first propose EdgeBoost, a meta-algorithm and framework that repeatedly applies a non-deterministic link predictor to improve the efficacy of community detection algorithms on networks with missing edges. On average EdgeBoost improves performance of existing algorithms by 7% on artificial data and 17% on ego networks collected from Facebook. The second system, Butterworth, identifies a social network user's topic(s) of interests and automatically generates a set of social feed ``rankers'' that enable the user to see topic specific sub-feeds. Butterworth uses link prediction to infer the missing semantics between members of a user's social network in order to detect topical clusters embedded in the network structure. For automatically generated topic lists, Butterworth achieves an average top-10 precision of 78%, as compared to a time-ordered baseline of 45%. Next, I propose Dobby, a system for constructing a knowledge graph of user-defined keyword tags. Leveraging a sparse set of labeled edges, Dobby trains a supervised learning algorithm to infer the hypernym relationships between keyword tags. Dobby was evaluated by constructing a knowledge graph of LinkedIn's skills dataset, achieving an average precision of 85% on a set of human labeled hypernym edges between skills. Lastly, I propose Lobbyback, a system that automatically identifies clusters of documents that exhibit text reuse and generates ``prototypes'' that represent a canonical version of text shared between the documents. Lobbyback infers a network structure in a corpus of documents and uses community detection in order to extract the document clusters.PhDComputer Science and EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/133443/1/mattburg_1.pd