Guided Walk: A Scalable Recommendation Algorithm for Complex Heterogeneous Social Networks

Online social networks have become predominant in recent years and have grown to encompass massive scales of data. In addition to data scale, these networks can be heterogeneous and contain complex structures between different users, between social entities and various interactions between users and social entities. This is especially true in enterprise social networks where hierarchies explicitly exist between employees as well. In such networks, producing the best recommendations for each user is a very challenging problem for two main reasons. First, the complex structures in the social network need to be properly mined and exploited by the algorithm. Second, these networks contain millions or even billions of edges making the problem very difficult computationally. In this paper we present Guided Walk, a supervised graph based algorithm that learns the significance of different network links for each user and then produces entity recommendations based on this learning phase. We compare the algorithm with a set of baseline algorithms using offline evaluation techniques as well as a user survey. The offline results show that the algorithm outperforms the next best algorithm by a factor of 3.6. The user survey further confirms that the recommendation are not only relevant but also rank high in terms of personal relevance for each user. To deal with large scale social networks, the Guided Walk algorithm is formulated as a Pregel program which allows us to utilize the power of distributed parallel computing. This would allow horizontally scaling the algorithm for larger social networks by simply adding more compute nodes to the cluster.