Zhendong Niu

Bio: Zhendong Niu is an academic researcher from Beijing Institute of Technology. The author has contributed to research in topics: Recommender system & Collaborative filtering. The author has an hindex of 28, co-authored 150 publications receiving 2645 citations. Previous affiliations of Zhendong Niu include University of Pittsburgh & University UCINF.

Knowledge-based recommendation: a review of ontology-based recommender systems for e-learning

Abstract: Recommender systems in e-learning domain play an important role in assisting the learners to find useful and relevant learning materials that meet their learning needs. Personalized intelligent agents and recommender systems have been widely accepted as solutions towards overcoming information retrieval challenges by learners arising from information overload. Use of ontology for knowledge representation in knowledge-based recommender systems for e-learning has become an interesting research area. In knowledge-based recommendation for e-learning resources, ontology is used to represent knowledge about the learner and learning resources. Although a number of review studies have been carried out in the area of recommender systems, there are still gaps and deficiencies in the comprehensive literature review and survey in the specific area of ontology-based recommendation for e-learning. In this paper, we present a review of literature on ontology-based recommenders for e-learning. First, we analyze and classify the journal papers that were published from 2005 to 2014 in the field of ontology-based recommendation for e-learning. Secondly, we categorize the different recommendation techniques used by ontology-based e-learning recommenders. Thirdly, we categorize the knowledge representation technique, ontology type and ontology representation language used by ontology-based recommender systems, as well as types of learning resources recommended by e-learning recommenders. Lastly, we discuss the future trends of this recommendation approach in the context of e-learning. This study shows that use of ontology for knowledge representation in e-learning recommender systems can improve the quality of recommendations. It was also evident that hybridization of knowledge-based recommendation with other recommendation techniques can enhance the effectiveness of e-learning recommenders.

A structural–functional basis for dyslexia in the cortex of Chinese readers

Abstract: Developmental dyslexia is a neurobiologically based disorder that affects ≈5–17% of school children and is characterized by a severe impairment in reading skill acquisition. For readers of alphabetic (e.g., English) languages, recent neuroimaging studies have demonstrated that dyslexia is associated with weak reading-related activity in left temporoparietal and occipitotemporal regions, and this activity difference may reflect reductions in gray matter volume in these areas. Here, we find different structural and functional abnormalities in dyslexic readers of Chinese, a nonalphabetic language. Compared with normally developing controls, children with impaired reading in logographic Chinese exhibited reduced gray matter volume in a left middle frontal gyrus region previously shown to be important for Chinese reading and writing. Using functional MRI to study language-related activation of cortical regions in dyslexics, we found reduced activation in this same left middle frontal gyrus region in Chinese dyslexics versus controls, and there was a significant correlation between gray matter volume and activation in the language task in this same area. By contrast, Chinese dyslexics did not show functional or structural (i.e., volumetric gray matter) differences from normal subjects in the more posterior brain systems that have been shown to be abnormal in alphabetic-language dyslexics. The results suggest that the structural and functional basis for dyslexia varies between alphabetic and nonalphabetic languages.

A hybrid recommendation algorithm adapted in e-learning environments

Abstract: With the rapid development of online learning technology, a huge amount of e-learning materials have been generated which are highly heterogeneous and in various media formats. Besides, e-learning environments are highly dynamic with the ever increasing number of learning resources that are naturally distributed over the network. On the other hand, in the online learning scenario, it is very difficult for users without sufficient background knowledge to choose suitable resources for their learning. In this paper, a hybrid recommender system is proposed to recommend learning items in users' learning processes. The proposed method consists of two steps: (1) discovering content-related item sets using item-based collaborative filtering (CF), and (2) applying the item sets to sequential pattern mining (SPM) algorithm to filter items according to common learning sequences. The two approaches are combined to recommend potentially useful learning items to guide users in their current learning processes. We also apply the proposed approach to a peer-to-peer learning environment for resource pre-fetching where a central directory of learning items is not available. Experiments are conducted in a centralized and a P2P online learning systems for the evaluation of the proposed method and the results show good performance of it.

Automatic construction of domain-specific sentiment lexicon based on constrained label propagation

Abstract: Domain-specific sentiment lexicon has played an important role in most practical opinion mining systems. Due to the ubiquitous domain diversity and absence of domain-specific prior knowledge, automatic construction of domain-specific sentiment lexicon has become a challenging research topic in recent years. This paper proposes a novel automatic construction strategy of domain-specific sentiment lexicon based on constrained label propagation. The candidate sentiment terms are extracted by leveraging the chunk dependency information and prior generic lexicon. The pairwise contextual and morphological constraints are defined and extracted between sentiment terms from the domain corpus, and are exploited as prior knowledge to improve the sentiment lexicon construction. The constraint propagation is applied to spread the effect of local constraints throughout the entire collection of candidate sentiment terms. The final propagated constraints are incorporated into the label propagation for the domain-specific sentiment lexicon construction. Experimental results on real-life datasets demonstrate that our approach to constrained label propagation could dramatically improve the performance of automatic construction of domain-specific sentiment lexicon.

Artificial neural networks

Abstract: Artificial neural networks (ANNs) constitute a class of flexible nonlinear models designed to mimic biological neural systems. In this entry, we introduce ANN using familiar econometric terminology and provide an overview of ANN modeling approach and its implementation methods. † Correspondence: Chung-Ming Kuan, Institute of Economics, Academia Sinica, 128 Academia Road, Sec. 2, Taipei 115, Taiwan; ckuan@econ.sinica.edu.tw. †† I would like to express my sincere gratitude to the editor, Professor Steven Durlauf, for his patience and constructive comments on early drafts of this entry. I also thank Shih-Hsun Hsu and Yu-Lieh Huang for very helpful suggestions. The remaining errors are all mine.