IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Knowledge and Data Engineering
Neighbor search with global geometry: a minimax message passing algorithm
Proceedings of the 24th international conference on Machine learning
A linear work, O(n1/6) time, parallel algorithm for solving planar Laplacians
SODA '07 Proceedings of the eighteenth annual ACM-SIAM symposium on Discrete algorithms
A topical PageRank based algorithm for recommender systems
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
A Generic Diffusion Kernel for Semi-supervised Learning
ISNN '08 Proceedings of the 5th international symposium on Neural Networks: Advances in Neural Networks
A Novel Recommending Algorithm Based on Topical PageRank
AI '08 Proceedings of the 21st Australasian Joint Conference on Artificial Intelligence: Advances in Artificial Intelligence
Modeling Collaborative Similarity with the Signed Resistance Distance Kernel
Proceedings of the 2008 conference on ECAI 2008: 18th European Conference on Artificial Intelligence
ItemRank: a random-walk based scoring algorithm for recommender engines
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
A random-walk based scoring algorithm applied to recommender engines
WebKDD'06 Proceedings of the 8th Knowledge discovery on the web international conference on Advances in web mining and web usage analysis
Enhancing link-based similarity through the use of non-numerical labels and prior information
Proceedings of the Eighth Workshop on Mining and Learning with Graphs
Commute time guided transformation for feature extraction
Computer Vision and Image Understanding
Challenging the long tail recommendation
Proceedings of the VLDB Endowment
Semi-metric Networks for Recommender Systems
WI-IAT '12 Proceedings of the The 2012 IEEE/WIC/ACM International Joint Conferences on Web Intelligence and Intelligent Agent Technology - Volume 03
Proceedings of the 7th ACM conference on Recommender systems
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This work presents a new perspective on characterizing the similarity between elements of a database or, more generally, nodes of a weighted, undirected, graph. It is based on a Markov-chain model of random walk through the database. The suggested quantities, representing dissimilarities (or similarities) between any two elements, have the nice property of decreasing (increasing) when the number of paths connecting those elements increases and when the "length" of any path decreases. The model is evaluated on a collaborative recommendation task where suggestions are made about which movies people should watch based upon what they watched in the past. The model, which nicely fits into the so-called "statistical relational learning" framework as well as the "link analysis" paradigm, could also be used to compute document or word similarities, and, more generally, could be applied to other database or web mining tasks.