Optimal Feature Selection for Context-Aware Recommendation using Differential Relaxation
Yong Wei Zheng, Robin Burke, Bamshad Mobasher · 2012
Research in context-aware recommender systems (CARS) usually requires the identification of the influential contextual variables in advance. In collaborative recommendation, there is a substantial trade-off between applying context very strictly and achieving good coverage and accuracy. Our prior work showed that this tradeoff can be managed by applying the contexts differentially in different components of the recommendation algorithm. In this paper, we extend our previous model and show that our differential context relaxation (DCR) model can also be used to identify demographic and item features that are linked to the contexts. We also demonstrate the application of binary particle swarm optimization as a scalable optimization technique for deriving the optimal relaxation.