Authors:
Jon Atle Gulla
1
;
Arne Dag Fidjestøl
1
;
Xiaomeng Su
2
and
Humberto Castejon
2
Affiliations:
1
Norwegian University of Science and Technology, Norway
;
2
Telenor Group, Norway
Keyword(s):
Recommender Systems, Personalization, User Profiling, Mobile News, Big Data, Information Retrieval.
Related
Ontology
Subjects/Areas/Topics:
Context-Awareness
;
Internet Technology
;
Mobile Information Systems
;
Personalized Web Sites and Services
;
Social Media Analytics
;
Society, e-Business and e-Government
;
User Modeling
;
Web Information Systems and Technologies
;
Web Interfaces and Applications
;
Web Services and Web Engineering
Abstract:
User profiling is an important part of content-based and hybrid recommender systems. These profiles model users’ interests and preferences and are used to assess an item’s relevance to a particular user. In the news domain it is difficult to extract explicit signals from the users about their interests, and user profiling depends on in-depth analyses of users’ reading habits. This is a challenging task, as news articles have short life spans, are unstructured, and make use of unclear and rapidly changing terminologies. This paper discusses an approach for constructing detailed user profiles on the basis of detailed observations of users’ interaction with a mobile news app. The profiles address both news categories and news entities, distinguish between long-term interests and running context, and are currently used in a real iOS mobile news recommender system that recommends news from 89 Norwegian newspapers.