Scalable Collaborative Filtering with Jointly Derived Neighborhood Interpolation Weights
ICDM '07 Proceedings of the 2007 Seventh IEEE International Conference on Data Mining
Collaborative prediction and ranking with non-random missing data
Proceedings of the third ACM conference on Recommender systems
Hybrid web recommender systems
The adaptive web
Performance of recommender algorithms on top-n recommendation tasks
Proceedings of the fourth ACM conference on Recommender systems
Proceedings of the sixth ACM conference on Recommender systems
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In a number of domains of interest for recommender systems, items are characterized by constrained and variable "capacity": the same product or service can be consumed by a limited number of users and the possibility of item consumption depends on contextual circumstances (e.g., time). Our work explores recommenders in the context of these "bounded" domains. We consider online hotel booking as a case study, and investigates if and how "missing" items (hotels that eventually becomes unavailable for users' consumption) affect the quality of recommendations. The paper proposes a technique for defining "missing" items as "best items", and presents an articulated empirical research in which recommendations for hotel online booking are evaluated in different experimental conditions with a user centric approach involving 142 participants.