Sentence examples for temporal habits from inspiring English sources

We can notice how the shopping temporal habits and regularities are driven by the age which assume markedly different distributions.

However, it has been given very few attention to the temporal dimension of shopping sessions considered on its own in order to extract a customer model which helps in understanding the purchase temporal habits.

These patterns should describe the customer's temporal habits highlighting when she typically makes a purchase in correlation with information about the amount of expenditure, number of purchased items and other similar aggregates.

The self-knowledge might lead the customer to change her temporal habits with the possibility of saving money in the case a more regular behavior brings to spend a lower amount of money.

As a consequence, by using methods proposed in the literature it is not possible to capture the temporal purchasing patterns of each customers, which put in correlation their temporal habits with other information such as the amount of expenditure and number of purchased items.

One of the main results of this temporal habit is that modern society has an only seemingly contradictory relationship with uncertainty.

Knowing the temporal purchasing habits the system can interact with your personal calendar and remind/alert you that in x days a certain amount is going to be spent.

Similarly, temporal variations in habit scores could reflect habit strength fluctuations or an unstable and unreliable measure.

First, our comparison of temporal stability between habit strength and previous behavior might be biased, since our measure of habit included several questions and our measure of previous behavior only one.

Data collected in a multi-tiered approach from SUPERB participants covered short-term, seasonal, and long-term changes in food consumption habits, temporal-spatial activity, and use of household and personal care products.

The temporal dimension of purchasing habits is exploited in [11] to understand how predictable are consumers in their merchant visitation patterns by using a Markov model for predicting the customer's next shop location.