iPoG: fast interactive proximity querying on graphs
Proceedings of the 18th ACM conference on Information and knowledge management
Lasso-based tag expansion and tag-boosted collaborative filtering
PCM'10 Proceedings of the Advances in multimedia information processing, and 11th Pacific Rim conference on Multimedia: Part II
Random walk based entity ranking on graph for multidimensional recommendation
Proceedings of the fifth ACM conference on Recommender systems
DPSP: distributed progressive sequential pattern mining on the cloud
PAKDD'10 Proceedings of the 14th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining - Volume Part II
Gateway finder in large graphs: problem definitions and fast solutions
Information Retrieval
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
Combining prestige and relevance ranking for personalized recommendation
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
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This paper presents an algorithm for recommending items using a diverse set of features. The items are recommended by performing a random walk on the k-partite graph constructed from the heterogenous features. To support personalized recommendation, the random walk must be initiated separately for each user, which is computationally demanding given the massive size of the graph. To overcome this problem, we apply multi-way clustering to group together the highly correlated nodes. A recommendation is then made by traversing the subgraph induced by clusters associated with a user's interest. Our experimental results on real data sets demonstrate the efficacy of the proposed algorithm.