| Summary: | 碩士 === 國立清華大學 === 通訊工程研究所 === 101 === Recommendation is the most popular tool to help users find the new items they are interested in. We study the link prediction problem on the author-conference network of DBLP data set, and we would like to predict what conferences the author will publish in. Collaborative filtering is the most common method to suggest items for users. However, the limitation of this approach is the sparsity problem. As a result, we perform the random walk on the graph to calculate the transition probability for predicting. We consider not only the bipartite graph but also the relationship of these authors, so we perform the random walk on this union of two graphs. Experimental results show it can predict more precisely when choosing the appropriate parameters in our algorithm, and it is useful with the information of the friendship.
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