Ranking in context-aware recommender systems
Minsuk Kahng, Sangkeun Lee, Sang‐goo Lee · 2011
As context is acknowledged as an important factor that can affect users' preferences, many researchers have worked on improving the quality of recommender systems by utilizing users' context. However, incorporating context into recommender systems is not a simple task in that context can influence users' item preferences in various ways depending on the application. In this paper, we propose a novel method for context-aware recommendation, which incorporates several features into the ranking model. By decomposing a query, we propose several types of ranking features that reflect various contextual effects. In addition, we present a retrieval model for using these features, and adopt a learning to rank framework for combining proposed features. We evaluate our approach on two real-world datasets, and the experimental results show that our approach outperforms several baseline methods.