Personalized Scientific Paper Recommendation Based on Heterogeneous Graph Representation

Ma, Xiao; Wang, Ranran

doi:10.1109/access.2019.2923293

Cited by 44 publications

(29 citation statements)

References 38 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To learn the process of feature regression, different citation features (e.g., title, abstract, keywords, citation count, author history) were extracted and incorporated with a topic model. A customized recommendation method based on heterogeneous graph was proposed by Ma et al, [30]. Based on the content of the articles, a user and a paper profiles were created (i.e., title, keywords, abstract).…”

Section: A Content Based Filtering Approach In Scholarly Recommendationmentioning

confidence: 99%

See 1 more Smart Citation

A Hybrid Personalized Scientific Paper Recommendation Approach Integrating Public Contextual Metadata

et al. 2021

View full text Add to dashboard Cite

Rapid increase in scholarly publications on the web has posed a new challenge to the researchers in finding highly relevant and important research articles associated with a particular area of interest. Even a highly relevant paper is sometimes missed especially for novice researchers due to lack of knowledge and experience in finding and accessing the most suitable articles. Scholarly recommender system is a very appropriate tool for this purpose that can enable researchers to locate relevant publications easily and quickly. However, the main downside of the existing approaches is that their effectiveness is dependent on priori user profiles and thus, they cannot recommend papers to the new users. Furthermore, the system uses both public and non-public metadata and therefore, the system is unable to find similarities between papers efficiently due to copyright restrictions. Considering the above challenges, in this research work, a novel hybrid approach is proposed that separately combines a Content Based Filtering (CBF) recommender module and a Collaborative Filtering (CF) recommender module. Unlike previous CBF and CF approaches, public contextual metadata and paper-citation relationship information are effectively incorporated into these two approaches separately to enhance the recommendation accuracy. In order to verify the effectiveness of the proposed approach, publicly available datasets were employed. Experimental results demonstrate that the proposed approach outperforms the baseline approaches in terms of standard metrics (precision, recall, F1-measure, mean average precision, and mean reciprocal rank), indicating that the proposed approach is more efficient in recommending scholarly publications.

show abstract

Section: A Content Based Filtering Approach In Scholarly Recommendationmentioning

confidence: 99%

“…The approaches presented in [10][11][12][23][24][25][26][27][28][29][30] depend on priori user profile to generate recommendation. As a result, they are unable to make suggestions to the new user.…”

Section: A Content Based Filtering Approach In Scholarly Recommendationmentioning

confidence: 99%

A Hybrid Personalized Scientific Paper Recommendation Approach Integrating Public Contextual Metadata

et al. 2021

View full text Add to dashboard Cite

show abstract

“…There have been many studies on the application of graph embedding-based recommendations in specific scenarios. Xiao Ma et al [33] proposed a recommendation method called HGRec to solve the recommendation problem of scientific papers. Xijun He et al [34] proposed a patent technology transaction recommendation model (PSR-VEC), which effectively solves the inactive trade phenomenon of patent products in the market.…”

Section: Graph Embedding-based Recommendationmentioning

confidence: 99%

A Novel Framework With Weighted Heterogeneous Educational Network Embedding for Personalized Freshmen Recommendation Under the Impact of COVID-19 Storm

et al. 2021

View full text Add to dashboard Cite

The global explosion of COVID-19 has brought unprecedented challenges to traditional higher education, especially for freshmen who have no major; they cannot determine what their real talents are. Thus, it is difficult for them to make correct choices based on their skills. Generally, existing methods mainly mine isomorphic information, ignoring relationships among heterogeneous information. Therefore, this paper proposes a new framework to give freshmen appropriate recommendations by mining heterogeneous educational information. This framework is composed of five stages: after data preprocessing, a weighted heterogeneous educational network (WHEN) is constructed according to heterogeneous information in student historical data. Then, the WHEN is projected into different subnets, on which metapaths are defined. Next, a WHEN-based embedding method is proposed, which helps mine the weighted heterogeneous information on multiple extended metapaths. Finally, with the information mined, a matrix factorization algorithm is used to recommend learning resources and majors for freshmen. A large number of experimental results show that the proposed framework can achieve better results than other baseline methods. This indicates that the proposed method is effective and can provide great help to freshmen during the COVID-19 storm.

show abstract

“…Scores are calculated based on these information and later top-n papers are recommended to a researcher. Ma and Wang [25] proposed a personalized recommendation method based on heterogeneous graph. User and paper profiles were created based on contents of papers (i.e., title, keywords, abstract).…”

Section: A Priori User Profile Based Techniquementioning

confidence: 99%

A Collaborative Approach Toward Scientific Paper Recommendation Using Citation Context

2020

View full text Add to dashboard Cite

Researchers face difficulties in finding relevant papers to their research interest as the number of scientific publication is rapidly increasing on the web. Scientific paper recommenders have emerged as a leading solution to help researchers by automatically suggesting relevant and useful publications. Several approaches have been proposed on improving recommender systems. However, most existing approaches depend on priori user profiles, and thus they cannot recommend papers to new user. Furthermore, the existing approaches utilize non-public contextual information, and thus it cannot adequately find similarities between papers due to copyright restrictions. Also, the existing approaches consider only single level paper-citation relation to identify similarities between papers. Considering the above challenges, this paper presents a collaborative filtering based recommendation approach for scientific papers that does not depend on priori user profiles and which utilizes only public contextual information. Using citation context, we utilized 2-level paper-citation relations to find hidden associations between papers. The rational underlying this approach is that, two papers are co-occurred with same cited paper(s) and two papers are co-occurring with same citing paper(s) are significantly similar to some extent. To evaluate the performance of the proposed approach, publicly available datasets are used to conduct extensive experiments. The experimental results demonstrate that the proposed approach has significantly outperforms the baseline approaches in terms of precision, recall, F1, mean average precision, and mean reciprocal rank, which are commonly used information retrieval metrics. The novelty of this study is that, with the proposed approach, researchers are able to find relevant and useful publications over the internet regardless of their previous research experiences and research area.

show abstract

Personalized Scientific Paper Recommendation Based on Heterogeneous Graph Representation

Cited by 44 publications

References 38 publications

A Hybrid Personalized Scientific Paper Recommendation Approach Integrating Public Contextual Metadata

A Hybrid Personalized Scientific Paper Recommendation Approach Integrating Public Contextual Metadata

A Novel Framework With Weighted Heterogeneous Educational Network Embedding for Personalized Freshmen Recommendation Under the Impact of COVID-19 Storm

A Collaborative Approach Toward Scientific Paper Recommendation Using Citation Context

Contact Info

Product

Resources

About