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P186 - I2CS 2011: 11th International Conference on Innovative Internet Community Systems

https://dl.gi.de/handle/20.500.12116/18976
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Auflistung P186 - I2CS 2011: 11th International Conference on Innovative Internet Community Systems nach Autor:in "Albayrak, Sahin"

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  • Konferenzbeitrag
    An architecture for smart semantic recommender applications
    (11th International Conference on Innovative Internet Community Systems (I2CS 2011), 2011) Lommatzsch, Andreas; Plumbaum, Till; Albayrak, Sahin
    With the growing availability of semantic datasets, the processing of such datasets becomes the focus of interest. In this paper, we introduce a new architecture that supports the aggregation of different types of semantic data and provides components for deriving recommendations and predicting relevant relationships between dataset entities. The developed system supports different types of data sources (e.g. databases, semantic networks) and enables the efficient processing of large semantic datasets with several different semantic relationship types. We discuss the presented architecture and describe an implemented application for the entertainment domain. Our evaluation shows that the architecture provides a powerful and flexible basis for building personalized semantic recommender systems.
  • Konferenzbeitrag
    Latent semantic social graph model for expert discovery in Facebook
    (11th International Conference on Innovative Internet Community Systems (I2CS 2011), 2011) Al-Kouz, Akram; Luca, Ernesto William de; Albayrak, Sahin
    Expert finding systems employ social networks analysis and natural language processing to identify candidate experts in organization or enterprise datasets based on a user's profile, her documents, and her interaction with other users. Expert discovery in public social networks such as Facebook faces the challenges of matching users to a wide range of expertise areas, because of the diverse human interests. In this paper we analyze the social graph and the user's interactions in the form of posts and group memberships to model user interests and fields of expertise. The proposed model reflects expertise and interests of users based on experimental analysis of the explicit and implicit social data in Online Social Networks (OSNs). It employs social networks analysis, text mining, text classification, and semantic text similarity techniques to analyze and discover the latent semantic social graph model that can express user's expertise. The proposed model also considers the semantic similarity between user's posts and his groups, Influence of friendship on group's membership, and Influence of friendship on user's posts. Experiments on the Facebook data show significant validity of the proposed model.