Learning to Probabilistically Identify Authoritative Documents

By David A. Cohn, Huan Chang. International Conference on Machine Learning, 2000

298 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/29f5da58639c526f6b76d6d189d440ebeabe914f

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Citations in the corpus

The Importance of Prior Probabilities for Entry Page Search (2002, unknown): “HITS and PageRank (or variants of those) are reported to be successful in numerous studies of retrieval quality [18, 3, 1, 25, 5], however when measuring relevance only, without taking quality or authoritativeness into account, HITS and PageRank based methods have not been able to improve retrieval…”
A Survey of Signed Network Mining in Social Media (2015, background): “Since negative links are usually not considered, most node ranking algorithms for unsigned networks cannot deal with negative values directly [Haveliwala 2002; Cohn and Chang 2000].”
The Missing Link - A Probabilistic Model of Document Content and Hypertext Connectivity (2000, methodology): “Linked and hyperlinked documents are generally composed of terms and citations; as such, both term-based PLSA and citation-based PHITS analyses are applicable.”
Higher-order Web link analysis using multilinear algebra (2005, background): “Unfortunately, Kleinberg [24] and others [4, 10] have observed that the authorities and hubs do not always match the original query due to “topic drift,” i.”
A Survey of Multi-View Representation Learning (2016, unknown): “In the original LinkPLSA model [9], both term-based PLSA [129] and citationbased PHITS [131] are merged into a joint probabilistic model, explaining terms and citations in terms of a common set of underlying factors.”
Mining Text Data (2012, methodology): “A method called sprinkling is proposed in [21], in which artificial terms are added to (or “sprinkled” into) the documents, which correspond to the class labels.”
A Survey on PageRank Computing (2005, methodology): “Finally, the probabilistic foundations of HITS and, in particular, the algorithm PHITS are discussed in [Cohn and Chang, 2000].”
Automatic Keyphrase Extraction via Topic Decomposition (2010, methodology): “This measure is inspired by the work in (Cohn and Chang, 2000).”
Combining content and link for classification using matrix factorization (2007, methodology): “In PLSI+PHITS, the link is constructed with the linkage from the topic of the source web page to the destination web page.”
A Survey of Eigenvector Methods for Web Information Retrieval (2005, methodology): “These methods include PHITS [19], SALSA [41], pSALSA [13], exponentiated HITS [26], and Randomized HITS [49].”
Generalized Latent Factor Models for Social Network Analysis (2011, methodology): “The link-based methods include: PHITS [Cohn and Chang, 2000], LDA-Link [Erosheva et al., 2004]–an extension of latent Dirichlet allocation (LDA) for link analysis, the popularity-based conditional link model (PCL) [Yang et al., 2009b], and the normalized cut (NCUT) for spectral clustering [Shi and…”
Bayesian reasoning and machine learning (2012, unknown): “• We have the two outcome counts ]a = [39, 26, 35] and ]b = [63, 12, 25].”