[Zurück]

M. von Wyl, B. Hofreiter, S. Marchand-Maillet:
"Serendipitous Exploration of Large-scale Product Catalogs";
Vortrag: IEEE Conference on Commerce and Enterprise Computing, Hangzhou, China; 09.09.2012 - 11.09.2012; in: "Proceedings of the 14th IEEE Conference on Commerce and Enterprise Computing (CEC 2012)", IEEE Computer Society, (2012), ISBN: 978-1-4673-6246-7.

Online shopping has developed to a stage where catalogs
have become very large and diverse. Thus, it is a challenge
to present relevant items to potential customers within a very few
interactions. This is even more so when users have no defined
shopping objectives but operate in an opportunistic mindset. This
problem is often tackled by recommender systems. However,
these systems rely on consistent user interaction patterns to
predict items of interest. In contrast, we propose to adapt the
classical information retrieval (IR) paradigm for the purpose
of accessing catalog items in a context of un-predictable user
interaction. Accordingly, we present a novel information access
strategy based on the notion of interest rather than relevance. We
detail the design of a scalable browsing system including learning
capabilities joint with a limited-memory model. Our approach
enables locating interesting items while not requiring good quality
descriptions within a few steps. Our system allows customer to
seamlessly change browsing objectives without having to start
explicitly a new session. An evaluation of our approach based on
both artificial and real-life datasets demonstrates its efficiency in
learning and adaptation.

Online shopping has developed to a stage where catalogs
have become very large and diverse. Thus, it is a challenge
to present relevant items to potential customers within a very few
interactions. This is even more so when users have no defined
shopping objectives but operate in an opportunistic mindset. This
problem is often tackled by recommender systems. However,
these systems rely on consistent user interaction patterns to
predict items of interest. In contrast, we propose to adapt the
classical information retrieval (IR) paradigm for the purpose
of accessing catalog items in a context of un-predictable user
interaction. Accordingly, we present a novel information access
strategy based on the notion of interest rather than relevance. We
detail the design of a scalable browsing system including learning
capabilities joint with a limited-memory model. Our approach
enables locating interesting items while not requiring good quality
descriptions within a few steps. Our system allows customer to
seamlessly change browsing objectives without having to start
explicitly a new session. An evaluation of our approach based on
both artificial and real-life datasets demonstrates its efficiency in
learning and adaptation.

Information access, user adaptation, e-commerce catalog, recommender systems