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Plsa Based Framework for Hybrid Social Recommender Systems
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Today, there are valuable sources of information concerning the personalized recommendation problem like user annotated internet sites, user interaction logs, online user communities. In the literature, hybrid social recommender systems have been proposed to reduce the sparsity of web usage data by integrating the user related information sources together. In this thesis, a method based on probabilistic latent semantic analysis is used as a framework for a hybrid social recommendation system. Different data hybridization approaches on probabilistic latent semantic analysis are experimented. Based on this flexible probabilistic model, network regularization and model blending approaches are applied on probabilistic latent semantic analysis model as a solution for social trust network usage throughout the collaborative filtering process. The proposed model has outperformed the baseline methods in our experiments. As a result of the research, it is shown that the proposed methods successfully model the rating and social trust data together in a theoretically principled way.
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