Post by account_disabled on Dec 24, 2023 3:55:17 GMT -5
Athose not beginning with ABC quickly the set of sequences to be tested diminishes. These are the broad outlines of the algorithm but you can find more detailed information in the original article. Summing up We have set ourselves an objective to identify informative subsequences on a conversion act. of information subsequence using mutual information. We have a routine for generating candidate sequences with the guarantee that they are frequent PrefixSpan. This step requires a minimum frequency of appearance and a possible maximum sequence length.
The process of extracting informative sequences consists of generating candidate sequences then accepting or rejecting them according to the criterion of mutual information or to a lesser extent ordering them according to the amount of information they provide. Analytics benefits From a descriptive point Phone Number List view This work makes it possible to develop an advanced sequence exploration product to complement the navigation module. An analyst could filter the sequences according to a minimal presence and analyse the effect of such sequences on the conversion rate. Alternatively they could keep only the sequences associated with the highest lowest conversion rates and take actions to promote avoid such sequences.
Finally by setting conversion and frequency thresholds any AT Internet user could quickly identify which are the key sequences of their visitors. From a predictive point of view As part of a project to identify hesitant shoppers we began by developing a model for learning purchasing behaviour based on the simple metrics we described at the beginning of this article page views visit time number of visits etc. Then as the model was iterated we injected variables into it to indicate the presence of sequences that had been selected as informative using the procedure described above. The addition of these variables allowed us to improve our prediction metrics by to depending on the cases and they were therefore retained.
The process of extracting informative sequences consists of generating candidate sequences then accepting or rejecting them according to the criterion of mutual information or to a lesser extent ordering them according to the amount of information they provide. Analytics benefits From a descriptive point Phone Number List view This work makes it possible to develop an advanced sequence exploration product to complement the navigation module. An analyst could filter the sequences according to a minimal presence and analyse the effect of such sequences on the conversion rate. Alternatively they could keep only the sequences associated with the highest lowest conversion rates and take actions to promote avoid such sequences.
Finally by setting conversion and frequency thresholds any AT Internet user could quickly identify which are the key sequences of their visitors. From a predictive point of view As part of a project to identify hesitant shoppers we began by developing a model for learning purchasing behaviour based on the simple metrics we described at the beginning of this article page views visit time number of visits etc. Then as the model was iterated we injected variables into it to indicate the presence of sequences that had been selected as informative using the procedure described above. The addition of these variables allowed us to improve our prediction metrics by to depending on the cases and they were therefore retained.