A unified framework of active transfer learning for cross-system recommendation
نویسندگان
چکیده
منابع مشابه
A unified framework of active transfer learning for cross-system recommendation
Article history: Received 8 May 2015 Received in revised form 16 December 2016 Accepted 23 December 2016 Available online 30 December 2016
متن کاملActive Transfer Learning for Cross-System Recommendation
Recommender systems, especially the newly launched ones, have to deal with the data-sparsity issue, where little existing rating information is available. Recently, transfer learning has been proposed to address this problem by leveraging the knowledge from related recommender systems where rich collaborative data are available. However, most previous transfer learning models assume that entity...
متن کاملSelective 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...
متن کاملActive manifold learning via a unified framework for manifold landmarking
The success of semi-supervised manifold learning is highly dependent on the quality of the labeled samples. Active manifold learning aims to select and label representative landmarks on a manifold from a given set of samples to improve semi-supervised manifold learning. In this paper, we propose a novel active manifold learning method based on a unified framework of manifold landmarking. In par...
متن کاملA Framework for Recommendation of courses in E-learning System
The course recommendation system in e-learning is a system that suggests the best combination of subjects in which the students are interested. In this paper, we propose a framework for recommendation of courses in the E-learning system. In our approach we collect the data for example student enrollment for a specific set of course. After getting data, we use different combination of algorithm ...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Artificial Intelligence
سال: 2017
ISSN: 0004-3702
DOI: 10.1016/j.artint.2016.12.004