Using Collaborative Models to Adaptively Predict Visitor Locations in Museums
AH '08 Proceedings of the 5th international conference on Adaptive Hypermedia and Adaptive Web-Based Systems
Using interest and transition models to predict visitor locations in museums
AI Communications - Recommender Systems
A hybrid SOFM-SVR with a filter-based feature selection for stock market forecasting
Expert Systems with Applications: An International Journal
Managing uncertainty in group recommending processes
User Modeling and User-Adapted Interaction
A meta-learning approach for selecting between response automation strategies in a help-desk domain
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 1
An empirical study of corpus-based response automation methods for an e-mail-based help-desk domain
Computational Linguistics
Layered evaluation of interactive adaptive systems: framework and formative methods
User Modeling and User-Adapted Interaction
Personalised rating prediction for new users using latent factor models
Proceedings of the 22nd ACM conference on Hypertext and hypermedia
Improving neighborhood based Collaborative Filtering via integrated folksonomy information
Pattern Recognition Letters
A user-and item-aware weighting scheme for combining predictive user models
UMAP'10 Proceedings of the 18th international conference on User Modeling, Adaptation, and Personalization
Utilising user texts to improve recommendations
UMAP'10 Proceedings of the 18th international conference on User Modeling, Adaptation, and Personalization
ACM Transactions on Interactive Intelligent Systems (TiiS)
User profiling vs. accuracy in recommender system user experience
Proceedings of the International Working Conference on Advanced Visual Interfaces
User effort vs. accuracy in rating-based elicitation
Proceedings of the sixth ACM conference on Recommender systems
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Recommender systems represent a class of personalized systems that aim at predicting a user's interest on information items available in the application domain, operating upon user-driven ratings on items and/or item features. One of the most widely used recommendation methods is collaborative filtering that exploits the assumption that users who have agreed in the past in their ratings on observed items will eventually agree in the future. Despite the success of recommendation methods and collaborative filtering in particular, in real-world applications they suffer from the insufficient number of available ratings, which significantly affects the accuracy of prediction. In this paper, we propose recommendation approaches that follow the collaborative filtering reasoning and utilize the notion of lifestyle as an effective user characteristic that can group consumers in terms of their behavior as indicated in consumer behavior and marketing theory. Emanating from a basic lifestyle-based recommendation algorithm we incrementally proceed to the development of hybrid recommendation approaches that address certain dimensions of the sparsity problem and empirically evaluate them providing further evidence of their effectiveness.