Probabilistic latent semantic analysis based framework for hybrid social recommender systems

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2010

Author

Eryol, Erkin

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Today, there are user annotated internet sites, user interaction logs, online user communities which are valuable sources of information concerning the personalized recommendation problem. In the literature, hybrid social recommender systems have been proposed to reduce the sparsity of the 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.

Subject Keywords

Computer enginnering., Recommender systems.

URI

http://etd.lib.metu.edu.tr/upload/2/12611921/index.pdf
https://hdl.handle.net/11511/19654

Collections

Graduate School of Natural and Applied Sciences, Thesis

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Citation Formats

E. Eryol, “Probabilistic latent semantic analysis based framework for hybrid social recommender systems,” M.S. - Master of Science, Middle East Technical University, 2010.