Situation-Aware Approach to Improve Context-based Recommender System
This work addresses the need for more personalized recommendations in context-aware systems, though it appears incremental by building on existing context-based methods.
The paper tackles the problem of improving context-based recommender systems by introducing a situation-aware approach that builds user profiles from social, spatial, and temporal contexts, and it proposes a method to dynamically adapt to user interest evolution.
In this paper, we introduce a novel situation aware approach to improve a context based recommender system. To build situation aware user profiles, we rely on evidence issued from retrieval situations. A retrieval situation refers to the social spatio temporal context of the user when he interacts with the recommender system. A situation is represented as a combination of social spatio temporal concepts inferred from ontological knowledge given social group, location and time information. User's interests are inferred from past user's interaction with the recommender system related to the identified situations. They are represented using concepts issued from a domain ontology. We also propose a method to dynamically adapt the system to the user's interest's evolution.