Yi Zhao's research while affiliated with Northeast University At Qinhuangdao Campus and other places

Publications (5)

Article
Full-text available
Author name disambiguation (AND) is a fundamental task in knowledge alignment for building a knowledge graph network or an online academic search system. Existing AND algorithms tend to cause over-splitting and over-merging problems of papers, severely jeopardizing the performance of downstream tasks. In this paper, we demonstrate the problem of pa...
Preprint
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Massive open online courses (MOOCs), which provide a large-scale interactive participation and open access via the web, are becoming a modish way for online and distance education. To help users have a better study experience, many MOOC platforms have provided the services of recommending courses to users. However, we argue that directly recommendi...
Article
Full-text available
In this era of exponential growth in the scale of data, information overload has become an urgent problem, and the use of increasingly flexible sensor cloud systems (SCS) for data collection has become a mainstream trend. Recommendation algorithms can search massive data sets to uncover information that meets the needs of users based on their inter...
Article
Full-text available
With the rising popularity of social networks and service recommendations, research on new methods of friend recommendation have become a key topic, especially when based on quality-driven resource processing in an edge computing environment. Traditional methods seldom systematically combine static attributes (e.g., interests, geographical location...
Article
Friend recommendation is a fundamental service in both social networks and practical applications, and is influenced by user behaviors such as interactions, interests, and activities. In this study, we first conduct in-depth investigations on factors that affect recommendation results. Next, we design Friend++, a hybrid multi-individual recommendat...

Citations

... Generally, user preferences can be roughly divided into long-term and short-term [32]. Long-term preferences often refer to the interests of users. ...
... In this situation, many machine learning and deep learning models are applied to recommendation systems. The authors in [14] design a hybrid deep neural network which combines attribute attention and network embedding to make recommendation with the help of both interactive semantics and contextual enhancement. Article [15] proposes a matrix factorization model with deep features learning which integrates a convolutional neural network. ...
... Several techniques for document modeling, knowledge retrieval, and techniques deriving information from the content of pages are proposed in many Web- Recommendation System Comparative Analysis: Internet of Things aided Networks 3 based customized applications such as e-commerce and elearning sites. User profiles are generally described as vectors in such applications so that each vector entry represents a weight or a degree of interest for each item on the Web pages [32]. ...