%0 Journal Article %1 dongshan2002markov %A Dongshan, Xing %A Junyi, Shen %C Piscataway, NJ, USA %D 2002 %I IEEE Educational Activities Department %J Computing in Science and Engineering %K chain diss inthesis markov model %N 6 %P 34--39 %R 10.1109/MCISE.2002.1046594 %T A New Markov Model For Web Access Prediction %U http://dx.doi.org/10.1109/MCISE.2002.1046594 %V 4 %X Web access prediction is an important research direction in Web mining. Markov models are well-suited for predicting Web access. Although higher-order Markov models have good predictions result, these models have several limitations associated with high state-space complexity and reduced coverage. These affect the prediction performance deeply. A new Web access prediction model, Hybrid-order Tree-like Markov Model (HTMM), is proposed in this article. The technique intelligently merges two methods: a tree-like Markov model method that aggregates the access sequences by pattern matching and a hybrid-order method that combines varying order Markov models so that the resulting model has a low state complexity, improved prediction accuracy, and retains high coverage. Experiments confirm its usefulness. It's suitable for applications in E-business, such as Web prefetching, link prediction, and recommendation.

@article{dongshan2002markov, abstract = {Web access prediction is an important research direction in Web mining. Markov models are well-suited for predicting Web access. Although higher-order Markov models have good predictions result, these models have several limitations associated with high state-space complexity and reduced coverage. These affect the prediction performance deeply. A new Web access prediction model, Hybrid-order Tree-like Markov Model (HTMM), is proposed in this article. The technique intelligently merges two methods: a tree-like Markov model method that aggregates the access sequences by pattern matching and a hybrid-order method that combines varying order Markov models so that the resulting model has a low state complexity, improved prediction accuracy, and retains high coverage. Experiments confirm its usefulness. It's suitable for applications in E-business, such as Web prefetching, link prediction, and recommendation.}, acmid = {766276}, added-at = {2017-01-30T15:02:20.000+0100}, address = {Piscataway, NJ, USA}, author = {Dongshan, Xing and Junyi, Shen}, biburl = {https://www.bibsonomy.org/bibtex/2cab772c0f75546b4014b33264a5079b2/becker}, doi = {10.1109/MCISE.2002.1046594}, interhash = {b5757865376e7ce86fe9a3848a1a8fab}, intrahash = {cab772c0f75546b4014b33264a5079b2}, issn = {1521-9615}, issue_date = {November 2002}, journal = {Computing in Science and Engineering}, keywords = {chain diss inthesis markov model}, month = nov, number = 6, numpages = {6}, pages = {34--39}, publisher = {IEEE Educational Activities Department}, timestamp = {2017-12-20T16:57:09.000+0100}, title = {A New Markov Model For Web Access Prediction}, url = {http://dx.doi.org/10.1109/MCISE.2002.1046594}, volume = 4, year = 2002 }