Cross-Domain Personalized Learning Resources Recommendation Method
نویسندگان
چکیده
منابع مشابه
Selective Transfer Learning for Cross Domain Recommendation
Collaborative filtering (CF) aims to predict users’ ratings on items according to historical user-item preference data. In many realworld applications, preference data are usually sparse, which would make models overfit and fail to give accurate predictions. Recently, several research works show that by transferring knowledge from some manually selected source domains, the data sparseness probl...
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Most recommender systems based on collaborative filtering aim to provide recommendations for a user in one domain. But data sparsity is a major problem for collaborative filtering techniques. Recently, many scholars have proposed recommendation models to alleviate the sparsity problem by transferring rating matrix in other domains. But different domains have different rating scales (e.g., ratin...
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Motivation Large-scale screenings of cancer cell lines with detailed molecular profiles against libraries of pharmacological compounds are currently being performed in order to gain a better understanding of the genetic component of drug response and to enhance our ability to recommend therapies given a patient's molecular profile. These comprehensive screens differ from the clinical setting in...
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ژورنال
عنوان ژورنال: Mathematical Problems in Engineering
سال: 2013
ISSN: 1024-123X,1563-5147
DOI: 10.1155/2013/958785