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Users can be linked to item data; item data may be linked to item data. With such a model, the task of recommending new items to users or making new connections between items can be undertaken by algorithms designed to establish the relatedness between vertices in a graph. One such class of algorithm is based on the random walk, whereby a sequence of connected vertices are visited based on an underlying probability distribution and a determination of vertex relatedness established. A <jats:italic>diffusion kernel<\/jats:italic> encodes such a process. This paper demonstrates several diffusion kernel approaches on a graph composed of user-item and item-item relationships. The approach presented in this paper, <jats:italic>RecWalk*<\/jats:italic>, consists of a user-item bipartite combined with an item-item graph on which several diffusion kernels are applied and evaluated in terms of <jats:italic>top-n recommendation<\/jats:italic>. We conduct experiments on several datasets of the RecWalk* model using combinations of different item-item graph models and personalised diffusion kernels. We compare accuracy with some non-item recommender methods. We show that diffusion kernel approaches match or outperform state-of-the-art recommender approaches.<\/jats:p>","DOI":"10.1007\/978-3-031-26438-2_23","type":"book-chapter","created":{"date-parts":[[2023,2,22]],"date-time":"2023-02-22T06:32:56Z","timestamp":1677047576000},"page":"292-304","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Graph-Based Diffusion Method for\u00a0Top-N Recommendation"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6661-6938","authenticated-orcid":false,"given":"Yifei","family":"Zhou","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9152-4138","authenticated-orcid":false,"given":"Conor","family":"Hayes","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,2,23]]},"reference":[{"key":"23_CR1","unstructured":"Aamazon product data (2018). https:\/\/jmcauley.ucsd.edu\/data\/amazon\/"},{"key":"23_CR2","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"27","DOI":"10.1007\/978-3-319-67810-8_3","volume-title":"Algorithms and Models for the Web Graph","author":"K Avrachenkov","year":"2017","unstructured":"Avrachenkov, K., Chebotarev, P., Rubanov, D.: Kernels on graphs as proximity measures. 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