Context-Aware Recommendation via Graph-Based Contextual Modeling and Postfiltering

Similarity-Based Context-Aware Recommendation

Context-aware recommender systems (CARS) take context into consideration when modeling user preferences. There are two general ways to integrate context with recommendation: contextual filtering and contextual modeling. Currently, the most effective context-aware recommendation algorithms are based on a contextual modeling approach that estimate deviations in ratings across different contexts. ...

Contextual eVSM: A Content-Based Context-Aware Recommendation Framework Based on Distributional Semantics

In several domains contextual information plays a key role in the recommendation task, since factors such as user location, time of the day, user mood, weather, etc., clearly affect user perception for a particular item. However, traditional recommendation approaches do not take into account contextual information, and this can limit the goodness of the suggestions. In this paper we extend the ...

Context-Aware Recommendation Based On Review Mining

Recommender systems are important building blocks in many of today’s e-commerce applications including targeted advertising, personalized marketing and information retrieval. In recent years, the importance of contextual information has motivated many researchers to focus on designing systems that produce personalized recommendations in accordance with the available contextual information of us...

Context-aware Mobile Recommendation System Based on Context History

Recommendation systems for the mobile Web have focused on endorsing specificr content based on user preferences. But, user preferences vary in different contexts, such as at different times of day and in different locations. Therefore, in a mobile networking setting, providing proactive personalized service is more likely to depend on actual user context. This paper proposed a context-aware mob...

Towards Context-Aware Social Recommendation via Trust Networks