A new non-negative matrix factorization method to build a recommender system
Publish place: Fourth International Conference on Modern Studies in Computer Science and Information Technology
Publish Year: 1396
نوع سند: مقاله کنفرانسی
زبان: English
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شناسه ملی سند علمی:
CONFITC04_081
تاریخ نمایه سازی: 6 مهر 1397
Abstract:
The main aim of this paper is to apply non-negative matrix factorization to build a recommender system. In a recommender system there are a group of users that rate to a set of items. These ratings can be represented by a rating matrix. The main problem is to estimate the unknown ratings and then predict the interests of the users to the items which haven’t rated. The main innovation of this paper is to propose a new algorithm to compute matrix factorization in a way that the factorized matrixes would be a good approximation for the initial rating matrix and moreover would be a good source to predict the unknown ratings of the items precisely. The results show that the proposed matrix factorization improves the estimated ratings considerably.
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Authors
Somaye Arabi Naree
Faculty of Mathematical Sciences and Computer, Kharazmi University, Taleghani Avenue, Tehran, Iran
Maryam Mohammadi
Faculty of Mathematical Sciences and Computer, Kharazmi University, Taleghani Avenue, Tehran, Iran