Yong Zheng
Robin Burke
ABSTRACT
Recommender systems have been used in many domains to assist users' decision making by providing item recommendations and thereby reducing information overload. Context-aware recommender systems go further, incorporating the variability of users' preferences across contexts, and suggesting items that are appropriate in different contexts. In this paper, we present a novel recommendation task, "Context Suggestion", whereby the system recommends contexts in which items may be selected. We introduce the motivations behind the notion of context suggestion and discuss several potential solutions. In particular, we focus specifically on user-oriented context suggestion which involves recommending appropriate contexts based on a user's profile. We propose extensions of well-known context-aware recommendation algorithms such as tensor factorization and deviation-based contextual modeling and adapt them as methods to recommend contexts instead of items. In our empirical evaluation, we compare the proposed solutions to several baseline algorithms using four real-world data sets.
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Index Terms
- User-Oriented Context Suggestion
Recommendations
Context suggestion: empirical evaluations vs user studies
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UMAP '17: Proceedings of the 25th Conference on User Modeling, Adaptation and PersonalizationContext suggestion refers to the task of recommending appropriate contexts to the users to improve the user experience. The suggested contexts could be time, location, companion, category, and so forth. In this paper, we particularly focus on the task ...
Context Recommendation Using Multi-label Classification
WI-IAT '14: Proceedings of the 2014 IEEE/WIC/ACM International Joint Conferences on Web Intelligence (WI) and Intelligent Agent Technologies (IAT) - Volume 02Context-aware recommender systems (CARS) are extensions of traditional recommenders that also take into account contextual condition of a user to whom a recommendation is made. The recommendation problem is, however, still focused on recommending a set ...
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