LDA-based document models for ad-hoc retrieval
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
Google news personalization: scalable online collaborative filtering
Proceedings of the 16th international conference on World Wide Web
Context-aware recommender systems
Proceedings of the 2008 ACM conference on Recommender systems
Learning to Rank for Information Retrieval
Foundations and Trends in Information Retrieval
Statistical models of music-listening sessions in social media
Proceedings of the 19th international conference on World wide web
Relevance and ranking in online dating systems
Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval
Proceedings of the fourth ACM conference on Recommender systems
Random walk based entity ranking on graph for multidimensional recommendation
Proceedings of the fifth ACM conference on Recommender systems
Ranking objects by following paths in entity-relationship graphs
Proceedings of the 4th workshop on Workshop for Ph.D. students in information & knowledge management
A generic graph-based multidimensional recommendation framework and its implementations
Proceedings of the 21st international conference companion on World Wide Web
Reducing the sparsity of contextual information for recommender systems
Proceedings of the sixth ACM conference on Recommender systems
Mining interests for user profiling in electronic conversations
Expert Systems with Applications: An International Journal
Adapting vector space model to ranking-based collaborative filtering
Proceedings of the 21st ACM international conference on Information and knowledge management
Exploiting enriched contextual information for mobile app classification
Proceedings of the 21st ACM international conference on Information and knowledge management
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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.