An effective graph-based diffusion method for top-n recommendation

The primary interest of this thesis is to design a graph-based recommendation approach to improve the recommendation result. The traditional recommendation approaches did not address data sparsity and insufficient information utilization issues. Consequently, we are motivated to build a user-item combination graph and apply the graph traversals on that graph to address these problems. Firstly, we use probabilistic graph traversals to solve the data sparsity problem by exploring the indirect relationships between users-to-users and items-to-items. Besides, we investigate graph kernels to effectively measure the similarity between a pair of graph nodes and combine the diffusion kernel with the graph-based recommendation approach. Then, to solve the insufficient information utilization problem, we aim to explore the item’s semantic information from knowledge graphs using deterministic graph traversals. We build the semantic inter-item graph and combine it with the graph-based recommendation approach to improve the recommendation result further. Finally, we experiment with our proposed methods on publicly well-known datasets to investigate the recommendation performance.

Publisher

University of Galway

Rights

Attribution-NonCommercial-NoDerivatives 4.0 International

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Collections

University of Galway Theses (Research Masters Theses)