iPoG: fast interactive proximity querying on graphs
Proceedings of the 18th ACM conference on Information and knowledge management
A modified random walk framework for handling negative ratings and generating explanations
ACM Transactions on Intelligent Systems and Technology (TIST) - Special section on twitter and microblogging services, social recommender systems, and CAMRa2010: Movie recommendation in context
Accurate and scalable nearest neighbors in large networks based on effective importance
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
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This paper studies how to incorporate side information (such as users' feedback) in measuring node proximity on large graphs. Our method (ProSIN) is motivated by the well-studied random walk with restart (RWR). The basic idea behind ProSIN is to leverage side information to refine the graph structure so that the random walk is biased towards/away from some specific zones on the graph. Our case studies demonstrate that ProSIN is well-suited in a variety of applications, including neighborhood search, center-piece subgraphs, and image caption. Given the potential computational complexity of ProSIN, we also propose a fast algorithm (Fast-ProSIN) that exploits the smoothness of the graph structures with/without side information. Our experimental evaluation shows that Fast-ProSIN achieves significant speedups (up to 49x) over straightforward implementations.