By Topic

Clustering to Deal with the New User Problem

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

2 Author(s)
Bouras, C. ; Comput. Eng. & Inf. Dept., Univ. of Patras & Comput. Technol. Inst. & Press Diophantus Patras, Patras, Greece ; Tsogkas, V.

Collaborative filtering (CF) techniques attempt to alleviate information overload by identifying which items a user will find interesting to browse. It focuses on identification of other users with similar tastes and usage of their opinions in order to recommend items. Commonly, however, CF suffers from the so-called new user problem which occurs when a new user is added to the system and there is not enough information to make a good suggestion. The system has to acquire some data about the new user in order to start making personalized recommendations. In this paper, we present a novel algorithm that combines previously acquired knowledge from article and user clustering in order to quickly determine the new user's interests. We attempt to address the new user problem by providing a personalized strategy for prompting the user with articles to rate. Our approach makes use of hypernyms extracted from the WordNet database and proves to be converging fast to the actual user interests based on minimal user ratings which are provided during the registration process.

Published in:

Computational Science and Engineering (CSE), 2012 IEEE 15th International Conference on

Date of Conference:

5-7 Dec. 2012