Authors:
Toon De Pessemier
;
Kris Vanhecke
and
Luc Martens
Affiliation:
Ghent University, Belgium
Keyword(s):
Recommender System, News, User Study, Context, User Interaction.
Related
Ontology
Subjects/Areas/Topics:
Context-Awareness
;
Enterprise Information Systems
;
Mobile Information Systems
;
Personalized Web Sites and Services
;
Recommendation Systems
;
Software Agents and Internet Computing
;
User Modeling
;
Web Information Systems and Technologies
;
Web Interfaces and Applications
Abstract:
Traditional recommender systems provide personal suggestions based on the user’s preferences, without taking
into account any additional contextual information such as time or device type. However, in many applications,
this contextual information may be relevant for the human decision process, and as a result, be
important to incorporate into the recommendation process, which gave rise to context-aware recommender
systems. However, the information value of contextual data for the recommendation process is highly dependent
on the application domain and the users’ consumption behavior in different contextual situations. This
research aims to assess the information value of context for a recommender system of a mobile news service by
analyzing user interactions and feedback. A large-scale user study shows that context-aware recommendations
outperform traditional recommendations, but also indicates that the accuracy improvement might be limited
in a real-life situation. Service usage ta
kes place in a limited number of different contexts due to user habits
and repetitive behavior, leaving little room for optimization based on the context. Data fragmentation over
different contextual situations strengthens the sparsity problem, thereby limiting the user-perceived accuracy
gain obtained by incorporating context in the recommender. These findings are important for news providers
when considering to offer context-aware recommendations.
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