Selective Markov models for predicting Web page accesses

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How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

A probabilistic model to resolve diversity–accuracy challenge of recommendation systems (2015, background): “Markov-based recommenders is a class of well-known and widely used techniques in real-world applications [8,9,24,31,41].”
Recommender Systems (2016, unknown): “The main idea in [182] is to propose the notion of selective Markov models, in which many of the irrelevant states are pruned up front during model construction.”
Data Mining for Web Personalization (2007, methodology): “Note however that the actual pruning process based on conﬁdence proposed by Deshpande and Karypis [35] is not the same as that carried out during sequence rule discovery.”
Recommender Systems in Technology Enhanced Learning (2011, methodology): “This is a common evaluation practice in systems examined for other domains (e.g. Breese et al. 1998; Deshpande and Karypis 2004; Papagelis et al. 2005; Herlocker et al. 2002), which indicate that careful testing and parameterisation has to be carried out before a recommender system is finally deploy”
Recurrent Recommender Networks (2017, unknown): “when modeling web browsing behavior [9].”
Outlier Analysis (2013, methodology): “Methods for anomaly detection in web logs are discussed in [140, 275].”