Cross-Domain Developer Recommendation Algorithm Based on Feature Matching

According to cross-domain personalized learning resources recommendation, a new personalized learning resources recommendation method is presented in this paper. Firstly, the cross-domain learning resources recommendation model is given. Then, a method of personalized information extraction from web logs is designed by making use of mixed interest measure which is presented in this paper. Finally, a learning resources recommendation algorithm based on transfer learning technology is presented. A time function and the weight constraint of wrong classified samples can be added to the classic TrAdaBoost algorithm. Through the time function, the importance of samples date can be distinguished. The weight constraint can be used to avoid the samples having too big or too small weight. So the Accuracy and the efficiency of algorithm are improved. Experiments on the real world dataset show that the proposed method could improve the quality and efficiency of learning resources recommendation services effectively.

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Low-Rank and Sparse Cross-Domain Recommendation Algorithm

Database Systems for Advanced Applications - Lecture Notes in Computer Science ◽

10.1007/978-3-319-91452-7_10 ◽

2018 ◽

pp. 150-157 ◽

Cited By ~ 3

Author(s):

Zhi-Lin Zhao ◽

Ling Huang ◽

Chang-Dong Wang ◽

Dong Huang

Keyword(s):

Low Rank ◽

Recommendation Algorithm ◽

Cross Domain

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An FM Developer Recommendation Algorithm by Considering Explicit Information and ID Information

Web Services – ICWS 2020 - Lecture Notes in Computer Science ◽

10.1007/978-3-030-59618-7_4 ◽

2020 ◽

pp. 49-60

Author(s):

Xu Yu ◽

Yadong He ◽

Biao Xu ◽

Junwei Du ◽

Feng Jiang ◽

...

Keyword(s):

Recommendation Algorithm ◽

Explicit Information ◽

Developer Recommendation

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Landmark Selection for Zero-shot Learning

Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence ◽

10.24963/ijcai.2019/338 ◽

2019 ◽

Author(s):

Yuchen Guo ◽

Guiguang Ding ◽

Jungong Han ◽

Chenggang Yan ◽

Jiyong Zhang ◽

...

Keyword(s):

Feature Matching ◽

Image Features ◽

Cross Domain ◽

The Arts ◽

Novel Approach ◽

Landmark Selection ◽

Selection For ◽

Treat All ◽

Heterogeneous Feature ◽

Compatibility Function

Zero-shot learning (ZSL) is an emerging research topic whose goal is to build recognition models for previously unseen classes. The basic idea of ZSL is based on heterogeneous feature matching which learns a compatibility function between image and class features using seen classes. The function is constructed based on one-vs-all training in which each class has only one class feature and many image features. Existing ZSL works mostly treat all image features equivalently. However, in this paper we argue that it is more reasonable to use some representative cross-domain data instead of all. Motivated by this idea, we propose a novel approach, termed as Landmark Selection(LAST) for ZSL. LAST is able to identify representative cross-domain features which further lead to better image-class compatibility function. Experiments on several ZSL datasets including ImageNet demonstrate the superiority of LAST to the state-of-the-arts.

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Word-Finding Support From Mobile Technology Benefits a Woman With Aphasia

Perspectives on Augmentative and Alternative Communication ◽

10.1044/aac24.1.26 ◽

2015 ◽

Vol 24 (1) ◽

pp. 26-39 ◽

Cited By ~ 1

Author(s):

Yvonne Gillette

Keyword(s):

Mobile Technology ◽

Clinical Education ◽

Augmentative And Alternative Communication ◽

Feature Matching ◽

Mobile Technologies ◽

Alternative Communication ◽

Alternative Intervention ◽

Communication Assessment ◽

Word Finding ◽

Intervention Costs

Mobile technology provides a solution for individuals who require augmentative and alternative intervention. Principles of augmentative and alternative communication assessment and intervention, such as feature matching and the participation model, developed with dedicated speech-generating devices can be applied to these generic mobile technologies with success. This article presents a clinical review of an adult with aphasia who reached her goals for greater communicative participation through mobile technology. Details presented include device selection, sequence of intervention, and funding issues related to device purchase and intervention costs. Issues related to graduate student clinical education are addressed. The purpose of the article is to encourage clinicians to consider mobile technology when intervening with an individual diagnosed with mild receptive and moderate expressive aphasia featuring word-finding difficulties.

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