Learning random walks to rank nodes in graphs
Proceedings of the 24th international conference on Machine learning
Bipartite graph reinforcement model for web image annotation
Proceedings of the 15th international conference on Multimedia
Recommendation via Query Centered Random Walk on K-Partite Graph
ICDM '07 Proceedings of the 2007 Seventh IEEE International Conference on Data Mining
TrustWalker: a random walk model for combining trust-based and item-based recommendation
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
The wisdom of the few: a collaborative filtering approach based on expert opinions from the web
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
Personalized tag recommendation using graph-based ranking on multi-type interrelated objects
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
ItemRank: a random-walk based scoring algorithm for recommender engines
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
A survey of collaborative filtering techniques
Advances in Artificial Intelligence
MyMediaLite: a free recommender system library
Proceedings of the fifth ACM conference on Recommender systems
A generic graph-based multidimensional recommendation framework and its implementations
Proceedings of the 21st international conference companion on World Wide Web
Collaborative personalized tweet recommendation
SIGIR '12 Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval
Proceedings of the sixth ACM conference on Recommender systems
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In this paper, we present an adaptive graph-based personalized recommendation method based on combining prestige and relevance ranking. By utilizing the unique network structure of n-partite heterogeneous graph, we attempt to address the problem of personalized recommendation in a two-layer ranking process with the help of reasonable measure of high and low order relationships by analyzing the representation of user's preference in the graph. With different initialization and surfing strategies, this graph-based ranking model can take different type of data into account to capture personal interests from multiple perspectives. The experiments show that this algorithm can achieve better performance than the traditional CF methods and some graph-based recommendation methods.