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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| 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 |
| Summary: | 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. |
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