A study of smoothing methods for language models applied to Ad Hoc information retrieval
Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval
Towards a Better Understanding of Context and Context-Awareness
HUC '99 Proceedings of the 1st international symposium on Handheld and Ubiquitous Computing
The Journal of Machine Learning Research
A study of mixture models for collaborative filtering
Information Retrieval
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
From Web to Social Web: Discovering and Deploying User and Content Profiles
Context-aware recommender systems
Proceedings of the 2008 ACM conference on Recommender systems
Latent class models for collaborative filtering
IJCAI'99 Proceedings of the 16th international joint conference on Artificial intelligence - Volume 2
Context-based splitting of item ratings in collaborative filtering
Proceedings of the third ACM conference on Recommender systems
Statistical models of music-listening sessions in social media
Proceedings of the 19th international conference on World wide web
Probabilistic latent semantic analysis
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
Build your own music recommender by modeling internet radio streams
Proceedings of the 21st international conference on World Wide Web
Playlist prediction via metric embedding
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
Context-aware music recommendation based on latenttopic sequential patterns
Proceedings of the sixth ACM conference on Recommender systems
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Context aware recommender systems go beyond the traditional personalized recommendation models by incorporating a form of situational awareness. They provide recommendations that not only correspond to a user's preference profile, but that are also tailored to a given situation or context. We consider the setting in which contextual information is represented as a subset of an item feature space describing short-term interests or needs of a user in a given situation. This contextual information can be provided by the user in the form of an explicit query, or derived implicitly. We propose a unified probabilistic model that integrates user profiles, item representations, and contextual information. The resulting recommendation framework computes the conditional probability of each item given the user profile and the additional context. These probabilities are used as recommendation scores for ranking items. Our model is an extension of the Latent Dirichlet Allocation (LDA) model that provides the capability for joint modeling of users, items, and the meta-data associated with contexts. Each user profile is modeled as a mixture of the latent topics. The discovered latent topics enable our system to handle missing data in item features. We demonstrate the application of our framework for article and music recommendation. In the latter case, the set of popular tags from social tagging Web sites are used for context descriptions. Our evaluation results show that considering context can help improve the quality of recommendations.