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نتیجه جستجو - عامل سازی ماتریس

تعداد مقالات یافته شده: 2
ردیف عنوان نوع
1 Coupled matrix factorization and topic modeling for aspect mining
عامل سازی ماتریسی جفت و مدلسازی عنوان برای واکاوی جنبه -2018
Aspect mining, which aims to extract ad hoc aspects from online reviews and predict rating or opinion on each aspect, can satisfy the personalized needs for evaluation of specific aspect on product quality. Recently, with the increase of related research, how to effectively integrate rating and review information has become the key issue for addressing this problem. Considering that matrix factorization is an effective tool for rating prediction and topic modeling is widely used for review processing, it is a natural idea to combine matrix factorization and topic modeling for aspect mining (or called aspect rating prediction). However, this idea faces several challenges on how to address suitable sharing factors, scale mismatch, and dependency relation of rating and review information. In this paper, we propose a novel model to effectively integrate Matrix factorization and Topic modeling for Aspect rating prediction (MaToAsp). To overcome the above challenges and ensure the performance, MaToAsp employs items as the sharing factors to combine matrix factorization and topic modeling, and introduces an interpretive preference probability to eliminate scale mismatch. In the hybrid model, we establish a dependency relation from ratings to sentiment terms in phrases. The experiments on two real datasets including Chinese Dianping and English Tripadvisor prove that MaToAsp not only obtains reasonable aspect identification but also achieves the best aspect rating prediction performance, compared to recent representative baselines.
keywords: Topic modeling | Matrix factorization | Aspect mining | Rating prediction
مقاله انگلیسی
2 A novel recommendation method based on social network using matrix factorization technique
یک روش نوین توصیه برمبنای شبکه اجتماعی با اسفاده از روش عامل سازی ماتریسی-2018
The rapid development of information technology and the fast growth of Internet have facilitated an explosion of information which has accentuated the information overload problem. Recommender systems have emerged in response to this problem and helped users to find their interesting contents. With increasingly complicated social context, how to fulfill personalized needs better has become a new trend in personalized recommendation service studies. In order to alleviate the sparsity problem of recommender systems meanwhile increase their accuracy and diversity in complex contexts, we propose a novel recommendation method based on social network using matrix factorization technique. In this method, we cluster users and consider a variety of complex factors. The simulation results on two benchmark data sets and a real data set show that our method achieves superior performance to existing methods.
keywords: Recommendation method |Social network |K-harmonic means |Particle swarm optimization |Matrix factorization
مقاله انگلیسی
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