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• W2066432005 abstract "The concept of random walk (RW) has been widely applied in the design of recommendation systems. RW-based approaches are effective in handling locality problem and taking extra information, such as the relationships between items or users, into consideration. However, the traditional RW-based approach has a serious limitation in handling bidirectional opinions. The propagation of positive and negative information simultaneously in a graph is nontrivial using random walk. To address the problem, this article presents a novel and efficient RW-based model that can handle both positive and negative comments with the guarantee of convergence. Furthermore, we argue that a good recommendation system should provide users not only a list of recommended items but also reasonable explanations for the decisions. Therefore, we propose a technique that generates explanations by backtracking the influential paths and subgraphs. The results of experiments on the MovieLens and Netflix datasets show that our model significantly outperforms state-of-the-art RW-based algorithms, and is capable of improving the overall performance in the ensemble with other models." @default.
• W2066432005 created "2016-06-24" @default.
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• W2066432005 date "2013-01-01" @default.
• W2066432005 modified "2023-10-17" @default.
• W2066432005 title "A modified random walk framework for handling negative ratings and generating explanations" @default.
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• W2066432005 doi "https://doi.org/10.1145/2414425.2414437" @default.
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