A Modified Random Walk Framework for Handling Negative Ratings and Generating Explanations

作者：Chen Yu Chih^*; Lin Yu Shi; Shen Yu Chun; Lin Shou De

来源：ACM Transactions on Intelligent Systems and Technology, 2013, 4(1).

DOI：10.1145/2414425.2414437

• Algorithms
• Experimentation
• Performance
• Ranking
• Collaborative Filtering
• Random Walk
• Integration
• Explanation

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.

• 出版日期2013-1