on 26-Sep-2022 (Mon)

Customer Shopping Pattern Model

We study the customer behaviour though time with equal time steps (intervals) as demonstrated in Figure 3. The time interval can be weekly, bi-weekly, monthly, or etc. Since the first purchase time among customers is different, we define a lower limit and upper limit during the time. The lower limit refers to the start point of our study and the upper limit refers to the end point of our study through time. In this case, we can define some equal time intervals between the lower and upper limits, as shown in Figure 3. The shopper’s purchase is then identified in each time interval and the R, F, and M variables are computed with respect to any point of interest. For example, if the point of interest is at t 4 , the recency is the time difference between the last purchase before the time of interest and the purchase itself. The time difference can be represented in scale of hour, day, week or etc., depending on the application. The frequency is the number of conducted purchases between the lower limit and the time of interest, which is F = 3 in this example. The monetary is the value customer has spent on the purchases between the lower limit and the time of interest. The R, F, and M values are computed for each customer with a CLN and for all times of interest.

These so-called continual or lifelong learning systems, and in particular lifelong deep neural networks (L-DNN), were inspired by brain neurophysiology. These deep learning algorithms separate feature training and rule training and are able to add new rule information on the fly.

While they still learn features slowly using a large and balanced data set, L-DDNs don't learn rules at this stage. And they don't need images of all known valve defects—the dataset can be relatively generic as long as the objects possess similar features (such as curves, edges, surface properties). With L-DNNs, this part of model creation can be done once, and without the help of the manufacturers.

What our hypothetical valve manufacturer needs to know is this: After the first step of feature learning is completed, they need only provide a small set of images of good valves for the system to learn a set of rules that define a good valve. There's no need to provide any images of defective valves. L-DNNs will learn on a single presentation of a small dataset using only “good" data (in other words, data about good ventilator valves), and then advise the user when an atypical product is encountered. This method is akin to the process humans use to spot differences in objects they encounter every day—an effortless task for us, but a very hard one for deep learning models until L-DNN systems came along.

Rather than needing thousands of varied images, L-DNNs only require a handful of images to train and build a prototypical understanding of the object. The system can be deployed in seconds, and the handful of images can even be collected after the L-DNN has been deployed and the “RUN" button has been pressed, as long as an operator ensures none of these images actually shows a product with defects. Changes to the rules that define a prototypical object can also be made in real time, to keep up with any changes in the production line.