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joshin4colours
joshin4colours
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This is a pretty interesting problem, actually :)

You could try using a Markov Chain Monte Carlo approach. I haven't worked with these types of models myself, but the idea is that you start with a given long-run distribution and use Monte Carlo modelling to develop the actual Markov chain. I know this is a pretty well-used technique for simulation calculations. If you don't know much about Monte Carlo methods, don't worry, the basic ideas are quite simple (and powerful).

You could also try a PageRank algorithm approach. The idea would be to construct your page links and initially assign equal weights based on the link structure (ie the user is equally likely to visit any possible linked page from the current page) and then assign a small chance that the user will visit any page uniformly randomly (they "surf" somehow to a random page uniformly at random). This can help develop a Markov chain with some realistic aspects of your system, such as that pages with more links are visited more often, but it's possible that every page can be visited at some point. The nice thing about models generated using a PageRank algorithm is that they lend themselves well to analysis, both computationally and analytically. (EDIT: I guess this method might not be exactly what you're looking for, since you already have the hit rates determined. But it might be helpful if you want to generate realistic "general" data for your hit rates.)

Hope this helps out.

This is a pretty interesting problem, actually :)

You could try using a Markov Chain Monte Carlo approach. I haven't worked with these types of models myself, but the idea is that you start with a given long-run distribution and use Monte Carlo modelling to develop the actual Markov chain. I know this is a pretty well-used technique for simulation calculations. If you don't know much about Monte Carlo methods, don't worry, the basic ideas are quite simple (and powerful).

You could also try a PageRank algorithm approach. The idea would be to construct your page links and initially assign equal weights based on the link structure (ie the user is equally likely to visit any possible linked page from the current page) and then assign a small chance that the user will visit any page uniformly randomly (they "surf" somehow to a random page uniformly at random). This can help develop a Markov chain with some realistic aspects of your system, such as that pages with more links are visited more often, but it's possible that every page can be visited at some point. The nice thing about models generated using a PageRank algorithm is that they lend themselves well to analysis, both computationally and analytically.

Hope this helps out.

This is a pretty interesting problem, actually :)

You could try using a Markov Chain Monte Carlo approach. I haven't worked with these types of models myself, but the idea is that you start with a given long-run distribution and use Monte Carlo modelling to develop the actual Markov chain. I know this is a pretty well-used technique for simulation calculations. If you don't know much about Monte Carlo methods, don't worry, the basic ideas are quite simple (and powerful).

You could also try a PageRank algorithm approach. The idea would be to construct your page links and initially assign equal weights based on the link structure (ie the user is equally likely to visit any possible linked page from the current page) and then assign a small chance that the user will visit any page uniformly randomly (they "surf" somehow to a random page uniformly at random). This can help develop a Markov chain with some realistic aspects of your system, such as that pages with more links are visited more often, but it's possible that every page can be visited at some point. The nice thing about models generated using a PageRank algorithm is that they lend themselves well to analysis, both computationally and analytically. (EDIT: I guess this method might not be exactly what you're looking for, since you already have the hit rates determined. But it might be helpful if you want to generate realistic "general" data for your hit rates.)

Hope this helps out.

Source Link
joshin4colours
joshin4colours
  • 2.2k
  • 1
  • 15
  • 28

This is a pretty interesting problem, actually :)

You could try using a Markov Chain Monte Carlo approach. I haven't worked with these types of models myself, but the idea is that you start with a given long-run distribution and use Monte Carlo modelling to develop the actual Markov chain. I know this is a pretty well-used technique for simulation calculations. If you don't know much about Monte Carlo methods, don't worry, the basic ideas are quite simple (and powerful).

You could also try a PageRank algorithm approach. The idea would be to construct your page links and initially assign equal weights based on the link structure (ie the user is equally likely to visit any possible linked page from the current page) and then assign a small chance that the user will visit any page uniformly randomly (they "surf" somehow to a random page uniformly at random). This can help develop a Markov chain with some realistic aspects of your system, such as that pages with more links are visited more often, but it's possible that every page can be visited at some point. The nice thing about models generated using a PageRank algorithm is that they lend themselves well to analysis, both computationally and analytically.

Hope this helps out.