Trust decomposition with classification in probabilistic matrix factorization for recommender systems
Date of Issue2019-04-26
School of Computer Science and Engineering
Trust has become more and more effective in the recommender system, which complements rating-based similarity to help to improve the final performance of rating prediction. However, trust cannot represent everything, e.g., trust may not prove that one will share the same preference on items. In my study, I focus on the trust decomposition in different specific classes of items, i.e., the action movie, horror movie, adventure movie and so on. Then, I will use the support vector regression method to combine all the trust aspects of the model to predict the latent trust value. Finally, I adopt them into the Probabilistic matrix factorization model for rating prediction in recommender systems. What’s more, the experiments on Epinions, Ciao, Douban, FilmTrust four datasets show there is an improvement of the performance of my model.
DRNTU::Engineering::Computer science and engineering