Trust decomposition with classification in probabilistic matrix factorization for recommender systems

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 m...

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Main Author: Yan, Yang
Other Authors: Pan Jialin, Sinno
Format: Theses and Dissertations
Language:English
Published: 2019
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Online Access:https://hdl.handle.net/10356/103417
http://hdl.handle.net/10220/48074
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Institution: Nanyang Technological University
Language: English
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spelling sg-ntu-dr.10356-1034172020-07-01T01:28:46Z Trust decomposition with classification in probabilistic matrix factorization for recommender systems Yan, Yang Pan Jialin, Sinno School of Computer Science and Engineering DRNTU::Engineering::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. Master of Engineering 2019-04-26T02:04:47Z 2019-12-06T21:12:14Z 2019-04-26T02:04:47Z 2019-12-06T21:12:14Z 2019 Thesis Yan, Y. (2019). Trust decomposition with classification in probabilistic matrix factorization for recommender systems. Master's thesis, Nanyang Technological University, Singapore. https://hdl.handle.net/10356/103417 http://hdl.handle.net/10220/48074 10.32657/10220/48074 en 59 p. application/pdf
institution Nanyang Technological University
building NTU Library
country Singapore
collection DR-NTU
language English
topic DRNTU::Engineering::Computer science and engineering
spellingShingle DRNTU::Engineering::Computer science and engineering
Yan, Yang
Trust decomposition with classification in probabilistic matrix factorization for recommender systems
description 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.
author2 Pan Jialin, Sinno
author_facet Pan Jialin, Sinno
Yan, Yang
format Theses and Dissertations
author Yan, Yang
author_sort Yan, Yang
title Trust decomposition with classification in probabilistic matrix factorization for recommender systems
title_short Trust decomposition with classification in probabilistic matrix factorization for recommender systems
title_full Trust decomposition with classification in probabilistic matrix factorization for recommender systems
title_fullStr Trust decomposition with classification in probabilistic matrix factorization for recommender systems
title_full_unstemmed Trust decomposition with classification in probabilistic matrix factorization for recommender systems
title_sort trust decomposition with classification in probabilistic matrix factorization for recommender systems
publishDate 2019
url https://hdl.handle.net/10356/103417
http://hdl.handle.net/10220/48074
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