ABSTRACT
As data in many disciplines continues to grow at a frantic pace, a more heterogeneous set of information sources both can and need to be connected in innovative ways to improve information search and recommendation systems. Using information retrieval (IR) as an example, classical search techniques are based on limited information, including query terms, the content of documents, and basic user characteristics. However, recent studies have started to investigate additional information resources, including semantic web data, knowledge graphs, linguistic features, user email account content, social networks, user behavior, the search context, and other sources. Intuitively, a heterogeneous graph integrating all these information sources could enhance search performance. However, the complexity of such graphs challenge most existing graph mining algorithms. In this study, we propose a novel approach for a personalized random walk over a complex heterogeneous graph; which we refer to as Personalized Graph Navigation (PGN). Unlike earlier expert-guided random walk approaches, by using an EM framework, PGN estimates the personalized usefulness probability distribution for each edge type (the latent variable), which can be used as a random walk navigation profile for a user on a heterogeneous graph. While PGN can cope with information retrieval/recommendation problems at a low cost, this method also transforms a complex heterogeneous graph into a homogeneous graph, allowing existing homogeneous graph mining algorithms to be applied to a heterogeneous graph.
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Index Terms
- Personalized Navigation and Random Walk on a Complex Heterogeneous Graph
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