IEEE Transactions on Knowledge and Data Engineering
A hybrid approach for improving predictive accuracy of collaborative filtering algorithms
User Modeling and User-Adapted Interaction
Adaptive, intelligent presentation of information for the museum visitor in PEACH
User Modeling and User-Adapted Interaction
Non-intrusive Personalisation of the Museum Experience
UMAP '09 Proceedings of the 17th International Conference on User Modeling, Adaptation, and Personalization: formerly UM and AH
Spatial processes for recommender systems
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
Using Keyword-Based Approaches to Adaptively Predict Interest in Museum Exhibits
AI '09 Proceedings of the 22nd Australasian Joint Conference on Advances in Artificial Intelligence
Hybrid web recommender systems
The adaptive web
Personalised pathway prediction
UMAP'10 Proceedings of the 18th international conference on User Modeling, Adaptation, and Personalization
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Hybridising user models can improve predictive accuracy However, research on linearly combining predictive user models (e.g., used in recommender systems) has often made the implicit assumption that the individual models perform uniformly across the user and item space, using static model weights when computing a weighted average of the predictions of the individual models This paper proposes a weighting scheme which combines user- and item-specific weight vectors to compute user- and item-aware model weights The proposed hybridisation approach adaptively estimates online the model parameters that are specific to a target user as information about this user becomes available Hence, it is particularly well-suited for domains where little or no information regarding the target user's preferences or interests is available at the time of offline model training The proposed weighting scheme is evaluated by applying it to a real-world scenario from the museum domain Our results show that in our domain, our hybridisation approach attains a higher predictive accuracy than the individual component models Additionally, our approach outperforms a non-adaptive hybrid model that uses static model weights.