DIS '97 Proceedings of the 2nd conference on Designing interactive systems: processes, practices, methods, and techniques
Understanding and Using Context
Personal and Ubiquitous Computing
Contextual recommender problems [extended abstract]
UBDM '05 Proceedings of the 1st international workshop on Utility-based data mining
The Turn: Integration of Information Seeking and Retrieval in Context (The Information Retrieval Series)
Context-Aware SVM for Context-Dependent Information Recommendation
MDM '06 Proceedings of the 7th International Conference on Mobile Data Management
What am I gonna wear?: scenario-oriented recommendation
Proceedings of the 12th international conference on Intelligent user interfaces
Generating semantically enriched user profiles for Web personalization
ACM Transactions on Internet Technology (TOIT)
Factorization meets the neighborhood: a multifaceted collaborative filtering model
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Experimental comparison of pre- vs. post-filtering approaches in context-aware recommender systems
Proceedings of the third ACM conference on Recommender systems
Context relevance assessment for recommender systems
Proceedings of the 16th international conference on Intelligent user interfaces
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
Empirical analysis of predictive algorithms for collaborative filtering
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
A Random Walk around the City: New Venue Recommendation in Location-Based Social Networks
SOCIALCOM-PASSAT '12 Proceedings of the 2012 ASE/IEEE International Conference on Social Computing and 2012 ASE/IEEE International Conference on Privacy, Security, Risk and Trust
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Investigations into combining context and recommendation has resulted in much fruitful research which has improved recommender systems. Such contextual information has come in many forms and been used in different ways, successfully offering better in-situ suggestions. Factors such as location, time of recommendation, etc. have proven themselves as useful contributors to exploiting context. One issue, however, is the importance placed on each aspect of context, especially as new forms of recommendation are developed. Context is traditionally incorporated into recommenders at design-time, as a filter or as an integral part of how users are modelled, but the importance placed on each aspect is not often examined. Social recommenders and systems that draw on the wealth of data present in social networks frequently have access to far more contextual factors than traditional recommenders, making user relationships to these factors all the more important. The main contribution of this paper is to provide an examination of contextual priorities from the social web, which prove useful to recommender research in the area. This ontological examination of context shows that users have different priorities when it comes to context with a large variation in the suitability of each contextual factor in predicting good recommendations. In addition, this paper presents and discusses an approach to individually tailoring context ontologies (allowing for dynamically generated context sets), evaluating contextual factors in recommending from the social web.