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In addition, customers state the budgets they are willing to spend on new clothes.
Customers may specify individual budgets for trousers, shirts, jackets, as well as
shoes. Further, the system requests customers to answer a sequence of multiple-
choice question. These questions ought to support systems in narrowing-down cus-
tomers' tastes.
Based on the data description, we will explain our user model in Sect.
8.5.1
and
the corresponding CBR in Sect.
8.5.2
.
8.5 Case-Based Recommender Approach
In this section, we will explain our case-based recommendation approach and the
underlying user model. Both, the approach and user model are fitted to the data
described before.
8.5.1 User Model
The model of the user preferences takes into account the findings of Sect.
8.2
which
emphasizes items' attributes such as colors, brands, or material and fitting and user
feedback, e.g., derived from the feedback field. The context, for instance leisure or
business, is currently not part of the model, as the dataset does not reflect this at the
moment.
The user model in this work follows the approach of overlay modeling of user
interests as discussed in Brusilovsky and Millan [
8
]. The overlay approach models
user knowledge or user interests as a subset of domain model. It is widely used in
adaptive education systems where the knowledge of a student is often modeled in
the form of “good/average/poor” knowledge for concept
X
, and
X
can be “math”
for instance. Adapting this idea for our scenario, the domain model is given by the
data we have about the clothes (see Sect.
8.4.1
). The user model is an adaptation of
that domain model. From the available user data, all orders of a user, a user model is
build containing the user preferences (positive or negative preferences) on different
abstraction layers.
The first layer is the top discrimination for clothes—the type of clothes. We have
identified ten different clothes categories:
•
Accessory
•
Jackets
•
Sakkos
•
Pullover
•
Shirts
•
Trousers
•
Shorts
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