Haiyang Zhang

Haiyang Zhang
The University of Sheffield | Sheffield · Department of Computer Science (Faculty of Engineering)

About

24
Publications
3,828
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78
Citations

Publications

Publications (24)
Article
Full-text available
Rapid and precise detection and classification of vehicles are vital for the intelligent transportation systems (ITSs). However, due to small gaps between vehicles on the road and interference features of photos, or video frames, containing vehicle images, it is difficult to detect and identify vehicle types quickly and precisely. For solving this...
Article
Full-text available
The automatic generation of a text summary is a task of generating a short summary for a relatively long text document by capturing its key information. In the past, supervised statistical machine learning was widely used for this Automatic Text Summarization (ATS) task, but due to its high dependence on the quality of text features, the generated...
Article
Full-text available
This paper proposes an RG hyperparameter optimization approach, based on a sequential use of random search (R) and grid search (G), for improving the blood glucose level prediction of boosting ensemble learning models. An indirect prediction of blood glucose levels in patients is performed, based on historical medical data collected by means of phy...
Conference Paper
Full-text available
Collaborative filtering (CF) has achieved great success in the field of recommender systems. In recent years, many novel CF models, particularly those based on deep learning or graph techniques, have been proposed for a variety of recommendation tasks, such as rating prediction and item ranking. These newly published models usually demonstrate thei...
Article
Full-text available
Based on a convolutional neural network (CNN) approach, this article proposes an improved ResNet-18 model for heartbeat classification of electrocardiogram (ECG) signals through appropriate model training and parameter adjustment. Due to the unique residual structure of the model, the utilized CNN layered structure can be deepened in order to achie...
Article
Full-text available
Matrix Factorization (MF) is one of the most successful Collaborative Filtering (CF) techniques used in recommender systems due to its effectiveness and ability to deal with very large user-item rating matrix. However, when the rating matrix sparseness increases its performance deteriorates. Expanding MF to include side-information of users and ite...
Conference Paper
Full-text available
It has been shown that word embeddings can exhibit gender bias, and various methods have been proposed to quantify this. However, the extent to which the methods are capturing social stereotypes inherited from the data has been debated. Bias is a complex concept and there exist multiple ways to define it. Previous work has leveraged gender word pai...
Preprint
Full-text available
Collaborative filtering (CF) has achieved great success in the field of recommender systems. In recent years, many novel CF models, particularly those based on deep learning or graph techniques, have been proposed for a variety of recommendation tasks, such as rating prediction and item ranking. These newly published models usually demonstrate thei...
Article
Full-text available
The accuracy of behavioral interactive features is a key factor for improving the performance of rating prediction. In order to deeply explore the potential rules of user behavior and enhance the accurate representation of interactive features, this paper proposes two rating prediction models, based on the spatial dimension and distance measurement...
Preprint
It has been shown that word embeddings can exhibit gender bias, and various methods have been proposed to quantify this. However, the extent to which the methods are capturing social stereotypes inherited from the data has been debated. Bias is a complex concept and there exist multiple ways to define it. Previous work has leveraged gender word pai...
Conference Paper
This paper presents a novel matrix factorization (MF) recommendation model, FeatureMF, which extends item latent vectors with item representation learned from metadata. By taking into account item features, the model addresses the coldstart item problem and data-sparsity problem of collaborative filtering (CF). Extensive experiments conducted on a...
Conference Paper
This paper presents a novel matrix factorization (MF) model, called FeatureMF, which takes into account item features and thus addresses the cold-start item and data sparsity problems of collaborative filtering (CF). More specifically, the model extends item latent vectors with item representation learned from metadata. Experiments conducted on a p...
Article
Full-text available
With the development of ubiquitous computing, recommendation systems have become essential tools in assisting users in discovering services they would find interesting. This process is highly dynamic with an increasing number of services, distributed over networks, bringing the problems of cold start and sparsity for service recommendation to a new...
Poster
Full-text available
Semantic Recommendation Prototype Adapted for the Ubiquitous Consumer Wireless World
Conference Paper
Full-text available
With the rapid growth of the Web, recommender systems have become essential tools to assist users to find high-quality personalized recommendations from massive information resources. Content-based filtering (CB) and collaborative filtering (CF) are the two most popular and widely used recommendation approaches. In this paper, we focus on ways of t...
Conference Paper
Recommendation systems employed on the Internet aim to serve users by recommending items which will likely be of interest to them. The recommendation problem could be cast as either a rating estimation problem which aims to predict as accurately as possible for a user the rating values of items which are yet unrated by that user, or as a ranking pr...
Conference Paper
Full-text available
This paper proposes an improvement to item recommendation systems based on collaborative filtering (CF) with implicit feedback data. Combined with the Bayesian Personalized Ranking (BPR) optimization approach, recommended for implicit-only feedback contexts, CF has been shown to be effective in generating accurate recommendations. The method, based...
Conference Paper
Exploiting additional item meta-data is proposed in this paper for solving data sparsity and cold start problems found in item-based collaborative filtering (CF) techniques, which are employed in recommendation systems. Additional item meta-data provides the foundation for generating a heterogeneous information network (HIN). The proposed approach...
Conference Paper
Full-text available
This paper describes the general service recommendation process matched to the telecommunication service delivery characteristics of the Ubiquitous Consumer Wireless World (UCWW). The goal is to provide consumers with the `best' service instances that match their dynamic, contextualized and personalized requirements and expectations, thereby aligni...
Conference Paper
Full-text available
The item-based collaborative filtering (CF) is one of the most successful approaches utilized by the recommendation systems. The basic concept behind it is to recommend those items to users which are similar to other items that these users have been interested in recently. This paper proposes a hybrid method that integrates user trust relations wit...
Conference Paper
Full-text available
Context-aware recommendation systems make recommendations by adapting to user's specific situation, and thus by exploring both the user preferences and the environment. In this paper, we propose a context-aware service recommendation framework utilising semantic knowledge in the Ubiquitous Consumer Wireless World (UCWW). The main objective of the f...
Conference Paper
Full-text available
Context-aware recommendation systems make recommendations by adapting to user's specific situation, and thus by exploring both the user preferences and the environment. In this paper, the design of a context-aware service recommendation framework utilising semantic knowledge in the Ubiquitous Consumer Wireless World (UCWW) is outlined. The main obj...
Conference Paper
Full-text available
This paper describes research into a new cloud-based service recommendation system for the Ubiquitous Consumer Wireless World (UCWW). The main objective of the system is to provide users with the 'best' service instances that match their dynamic, contextualised and personalised requirements and expectations, thereby achieving the goal of the always...

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Project (1)
Project
The project encompasses work on building models for the recommendation of services to users