The Intelligent surfer: Probabilistic Combination of Link and Content Information in PageRank

By Matthew Richardson, Pedro M. Domingos. Neural Information Processing Systems, 2001

441 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/0f1a73fc03b740b38d29278872366a44f877f56a

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Citations in the corpus

Graph neural networks for ranking Web pages (2005, background): “While PageRank and most of the other page ranking algorithms are designed to serve general purpose search engines, some recent approaches provide specialized rankings which are suited for particular requirements [6, 12, 13, 17 , 20].”
Deeper Inside PageRank (2004, background): “Several groups have suggested incorporating text information into the link analysis [16, 38, 60, 67, 103].”
The Perron-Frobenius theorem: some of its applications (2005, unknown): “There has been a lot of recent interest in link-based ranking techniques that consider these aspects [16], [22], [12], [14].”
Trust Management for the Semantic Web (2003, methodology): “The behavior of the random knowledge-surfer is very similar to that of the intelligent surfer presented in [32], which is a generalization of PageRank that allows non-uniform transitions between web pages.”
Scaling personalized web search (2003, background): “Unlike HITS, [12] suggested that importance scores be precomputed ofﬂine for every possible text query, but the enormous number of possibilities makes this approach difﬁcult to scale.”
Universal Option Models (2014, background): “Even article recommendation can be viewed as a multiple-reward planning problem, where each user query has an associated reward function and the relevance of an article is given by walking over the links between the articles [Page et al., 1998, Richardson and Domingos, 2002].”
EigenRank: a ranking-oriented approach to collaborative filtering (2008, methodology): “A few follow up works to PageRank proposed different methods to bias the personalization vector v to take into account different types of information besides the link structure such as contents[10, 23] and user preferences[14].”
Adaptive methods for the computation of PageRank (2004, background): “Secondly, recent approaches to personalized and topic-sensitive PageRank schemes [3,6,9] require computing many PageRank vectors, each biased towards certain types of pages.”
Automatic identification of user interest for personalized search (2006, methodology): “After the original PageRank paper [11] was published, there has been considerable research in Personalized PageRank [5, 12, 13, 14, 15].”
Extrapolation methods for accelerating PageRank computations (2003, background): “Furth ermore, recent approaches to personalized and topic-sensiti ve PageRank schemes [11, 20, 14] require computing manyPageRank vectors, each biased towards certain types of pages.”
TripleRank: Ranking Semantic Web Data by Tensor Decomposition (2009, background): “based on appearance of particular terms in Web documents [31,29], content classification [13,19], structural properties like in-domain vs.”
Photonic matrix multiplication lights up photonic accelerator and beyond (2022, methodology): “Matrix computation is one of the most widely used and indispensable tools of information processing in science and engineering 4,5 .”
Exploiting the Block Structure of the Web for Computing (2003, background): “Secondly, recent approaches to personalized and topicsensitive PageRank schemes [8, 10, 16] require comput-”
ObjectRank: Authority-Based Keyword Search in Databases (2004, methodology): “Keyword search in databases has some unique characteristics, which make the straightforward application of the random walk model as described in previous work [8, 18, 26] inadequate.”
Yes, there is a correlation: - from social networks to personal behavior on the web (2008, background): “This "Web homophily" can be used to advantage in, for example, finding communities of Web pages [9][10] and the ranking of Web pages [17].”