A modified random walk framework for handling negative ratings and generating explanations

Authors:
Yu-Chih Chen;Yu-Shi Lin;Yu-Chun Shen;Shou-De Lin
Affiliations:
National Taiwan University, Taipei;National Taiwan University, Taipei;National Taiwan University, Taipei;National Taiwan University, Taipei
Venue:
ACM Transactions on Intelligent Systems and Technology (TIST) - Special section on twitter and microblogging services, social recommender systems, and CAMRa2010: Movie recommendation in context
Year:
2013

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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.