Authors:
Rabaa Alabdulrahman
1
;
Herna Viktor
1
and
Eric Paquet
2
Affiliations:
1
School of Electrical Engineering and Computer Science, University of Ottawa, Ottawa and Canada
;
2
School of Electrical Engineering and Computer Science, University of Ottawa, Ottawa, Canada, National Research Council of Canada, Ottawa and Canada
Keyword(s):
Recommendation Systems, Collaborative Filtering, Cold-start, Active Learning.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Clustering and Classification Methods
;
Collaborative Filtering
;
Computational Intelligence
;
Data Mining in Electronic Commerce
;
Evolutionary Computing
;
Knowledge Discovery and Information Retrieval
;
Knowledge-Based Systems
;
Machine Learning
;
Soft Computing
;
Symbolic Systems
;
User Profiling and Recommender Systems
Abstract:
Recommendation systems, which are employed to mitigate the information overload faced by e-commerce users, have succeeded in aiding customers during their online shopping experience. However, to be able to make accurate recommendations, these systems require information about the items for sale and information about users’ individual preferences. Making recommendations to new customers, with no prior data in the system, is therefore challenging. This scenario, called the “cold-start problem,” hinders the accuracy of recommendations made to a new user. In this paper, we introduce the popular users personalized predictions (PUPP) framework to address cold-starts. In this framework, soft clustering and active learning is used to accurately recommend items to new users. Experimental evaluation shows that the PUPP framework results in high performance and accurate predictions. Further, focusing on frequent, or so-called “popular,” users during our active-learning stage clearly benefits th
e learning process.
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