Information Technology Reference
In-Depth Information
Chapter 8
Personalized Fashion Advice
Till Plumbaum and Benjamin Kille
Abstract
Shopping online for clothes is becoming very popular recently. But finding
good clothes remains a difficult task. We face a wealth of clothes on offer, and with-
out the possibility to fit or feel the product, making decisions is not easy. In this
chapter, we present a use case of an online retailer that aims to improve the shopping
experience of men. Differing from conventional online shops where the customers
browse through various products and eventually add items to their shopping basket,
this shopping service relies on the expertise of fashion advisers who, after getting
in contact with the costumers, arrange a combination of different clothes and ship
them to the customers. We present a case-based recommendation approach using
the available user information entered explicitly, such as price constraints and pre-
ferred colors, and also learn a user model based on purchase histories. We evaluate
and compare our case-based approach with standard recommendation approaches.
The evaluation shows that even with little knowledge, a suitable user model can be
learned and used for computing recommendations. The evaluation bases on real data
of customers of an online shop. Based on the results, using a case-based recommen-
dation approach could help to solve cold-start problems. But for computing good
recommendations for all users, more information about explicit user preferences is
needed, which is currently not available.
Steven Marks Goes Shopping
It was a cold and stormy night in November. Steven was sitting at the big kitchen
table, a refurbished heirloom from his great grandmother's house, drinking a cup of
black, fair trade coffee. Finally some alone-time for him. Suzanne and the kids were
visiting Suzanne's family at the coast, not returning till end of next week. Steven was
on his own for the next few days. “So Steven, what will you do today?”, he said to
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