188/4 E-Commerce Group
Institute of Software Technology and Interactive Systems
Vienna University of Technology
Favoritenstrasse 9-11/188, A-1040 Vienna, Austria

Serendipitous Exploration of Large-scale Product Catalogs

Type: 
Speech with CD or web proceedings
Abstract: 
Online shopping has developed to a stage where catalogs<br> have become very large and diverse. Thus, it is a challenge<br> to present relevant items to potential customers within a very few<br> interactions. This is even more so when users have no defined<br> shopping objectives but operate in an opportunistic mindset. This<br> problem is often tackled by recommender systems. However,<br> these systems rely on consistent user interaction patterns to<br> predict items of interest. In contrast, we propose to adapt the<br> classical information retrieval (IR) paradigm for the purpose<br> of accessing catalog items in a context of un-predictable user<br> interaction. Accordingly, we present a novel information access<br> strategy based on the notion of interest rather than relevance. We<br> detail the design of a scalable browsing system including learning<br> capabilities joint with a limited-memory model. Our approach<br> enables locating interesting items while not requiring good quality<br> descriptions within a few steps. Our system allows customer to<br> seamlessly change browsing objectives without having to start<br> explicitly a new session. An evaluation of our approach based on<br> both artificial and real-life datasets demonstrates its efficiency in<br> learning and adaptation.
Hidden Keywords: 
Department Focus: 
Business Informatics
TU Focus: 
Computational Science and Engineering
Proceedings: 
Proceedings of the 14th IEEE Conference on Commerce and Enterprise Computing (CEC 2012)
Publisher: 
IEEE Computer Society
Year: 
2012
ISBN: 
ISBN: 978-1-4673-6246-7
Pages: 
Accepted: 
Acceptance undecided
Invited: 
Reference: 
M. von Wyl, B. Hofreiter, <u>S. Marchand-Maillet</u>: <br>"<i>Serendipitous Exploration of Large-scale Product Catalogs</i>"; <br>Talk: IEEE Conference on Commerce and Enterprise Computing, Hangzhou, China; 09-09-2012 - 09-11-2012; in: "<i>Proceedings of the 14th IEEE Conference on Commerce and Enterprise Computing (CEC 2012)</i>", IEEE Computer Society, (2012), ISBN: 978-1-4673-6246-7.<br><br> <a href="https://publik.tuwien.ac.at/showentry.php?ID=209064&lang=2" class="publist"><i>More information</i></a><br><br>
Abstract German: 
Online shopping has developed to a stage where catalogs<br> have become very large and diverse. Thus, it is a challenge<br> to present relevant items to potential customers within a very few<br> interactions. This is even more so when users have no defined<br> shopping objectives but operate in an opportunistic mindset. This<br> problem is often tackled by recommender systems. However,<br> these systems rely on consistent user interaction patterns to<br> predict items of interest. In contrast, we propose to adapt the<br> classical information retrieval (IR) paradigm for the purpose<br> of accessing catalog items in a context of un-predictable user<br> interaction. Accordingly, we present a novel information access<br> strategy based on the notion of interest rather than relevance. We<br> detail the design of a scalable browsing system including learning<br> capabilities joint with a limited-memory model. Our approach<br> enables locating interesting items while not requiring good quality<br> descriptions within a few steps. Our system allows customer to<br> seamlessly change browsing objectives without having to start<br> explicitly a new session. An evaluation of our approach based on<br> both artificial and real-life datasets demonstrates its efficiency in<br> learning and adaptation.