Zusammenfassung
Online retailers have access to large amounts of transactional
data but current recommender systems tend to be short-sighted in nature
and usually focus on the narrow problem of pushing a set of closely
related products that try to satisfy the user's current need. Most ecommerce
recommender systems analyze a large amount of transactional
data without actually having any idea of what the items in the transactions
mean or what they say about the customers who purchased or
browsed those items. In this paper, we present a case study of a system
that recommends items based on a custom-built knowledge base that
consists of products and associated semantic attributes. Our system rst
extracts semantic features that characterize the domain of interest, apparel
products in our case, using text learning techniques and populates
a knowledge base with these products and features. The recommender
system analyzes descriptions of products that the user browses or buys
and automatically infers these semantic attributes to build a model of the
user. This abstraction allows us to not only recommend other items in
the same class of products that "match" the user model but also gives us
the ability to understand the customer's "tastes" and recommend items
across categories for which traditional collaborative ltering and contentbased
systems are unsuitable. Our approach also allows us to "explain"
the recommendations in terms of qualitative features which, we believe,
enhances the user experience and helps build the user's condence in the
recommendations.
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