Machine Learning
Mediation of user models for enhanced personalization in recommender systems
User Modeling and User-Adapted Interaction
Learning classifiers from only positive and unlabeled data
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Is more always merrier?: a deep dive into online social footprints
Proceedings of the 2012 ACM workshop on Workshop on online social networks
Aggregated, interoperable and multi-domain user profiles for the social web
Proceedings of the 8th International Conference on Semantic Systems
Improving business rating predictions using graph based features
Proceedings of the 19th international conference on Intelligent User Interfaces
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The tremendous popularity of Online Social Networks (OSN) has led to situations, where users have their profiles spread across multiple networks. These partial profiles reflect different user characteristics, depending mainly on the nature of the network, e.g., Facebook's social vs. LinkedIn's professional focus. Combining data gathered by multiple networks may benefit individual users, and the community as a whole, as this could facilitate the provision of more accurate services and recommendations. This paper reports on an exploratory study of the process of making such recommendations using a unique multi-network dataset containing user interests across multiple domains, e.g., music, books, and movies. We represent the data using a graph model and generate recommendations using a set of features extracted from and populated by the model. We assess the contribution of various network- and domain-related features to the accuracy of the recommendations and motivate future work into automated feature selection.