Browsing by Author "Sun, J"

Now showing 1 - 4 of 4

item: Article-Full-text
Identifying Social Roles Using Heterogeneous Features in Online Social Networks
(Wiley-Blackwell Publishing Ltd, 2019) Liu, Y; Du, F; Sun, J; Silva, T; Jiang, Y; Zhu, T
Role analysis plays an important role when exploring social media and knowledge-sharing platforms for designing marking strategies. However, current methods in role analysis have overlooked content generated by users (e.g., posts) in social media and hence focus more on user behavior analysis. The user-generated content is very important for characterizing users. In this paper, we propose a novel method which integrates both user behavior and posted content by users to identify roles in online social networks. The proposed method models a role as a joint distribution of Gaussian distribution and multinomial distribution, which represent user behavioral feature and content feature respectively. The proposed method can be used to determine the number of roles concerned automatically. The experimental results show that the proposed method can be used to identify various roles more effectively and to get more insights on such characteristics.
item: Conference-Full-text
A Novel approach for personalized article recommendation in online scientific communities
(2015-06-09) Sun, J; Ma, J; Liu, X; Liu, Z; Wang, G; Jiang, H; Silva, ATP
Rapid proliferation of information technologies has generated sheer volume of information which makes scientific research related information searching more challenging. Personalized recommendation is the widely adopted technique to recommend relevant documents to researchers. Current methods are suffering from mismatch problem and match irrelevance problem and fail to generate highly related results. To overcome these problems, we propose a novel approach to recommend articles to the researchers. In our approach we integrate three types of similarity measures: keyword similarity, journal similarity, and author similarity to measure the relevance of the articles to researchers. The keyword similarity is used to generate candidate list of articles, and the journal similarity and author similarity are used to select most suitable articles from the candidate list. The integrated similarity measure is used to rank the articles based on their relevance. The proposed method is implemented in Scholar Mate (www.scholarmate.com), the online research social network platform. The evaluation results exhibit that proposed method is more effective than existing ones.
item: Conference-Full-text
Phytocapping as a cost-effective and sustainable cover option for waste disposal sites in developing countries
(2013-11-18) Yuen, STS; Michael, RN; Salt, M; Jaksa, MB; Sun, J
Few waste disposal sites in developing countries are designed and operated as engineered sanitary landfills due to common technical and financial constraints. Phytocapping presents a natural soil-plant alternative to the conventional engineered landfill cover design. It requires less engineering input and has a lower cost than conventional impermeable covers as it only utilizes local recourses. It also offers the advantage of oxidating methane to reduce landfill greenhouse emissions. This type of covers has the potential to make a significant difference in the way that developing countries are capping their waste sites. This paper introduces the phytocap concept as well as discusses its relevance and advantages for developing countries.
item: Article-Full-text
Topic-based hierarchical Bayesian linear regression models for niche items recommendation
(SAGE Publications Inc, 2019) Liu, Y; Xiong, Q; Sun, J; Jiang, Y; Silva, T; Ling, H
A vital research concern for a personalised recommender system is to target items in the long tail. Studies have shown that sales of the e-commerce platform possess a long-tail character, and niche items in the long tail are challenging to be involved in the recommendation list. Since niche items are defined by the niche market, which is a small market segment, traditional recommendation algorithms focused more on popular items promotion and they do not apply to the niche market. In this article, we aim to find the best users for each niche item and proposed a topic-based hierarchical Bayesian linear regression model for niche item recommendation. We first identify niche items and build niche item subgroups based on descriptive information of items. Moreover, we learn a hierarchical Bayesian linear regression model for each niche item subgroup. Finally, we predict the relevance between users and niche items to provide recommendations. We perform a series of validation experiments on Yahoo Movies dataset and compare the performance of our approach with a set of representative baseline recommender algorithms. The result demonstrates the superior performance of our recommendation approach for niche items.