A Hybrid Approach Toward Research Paper Recommendation Using Centrality Measures and Author Ranking

Abstract

The volume of research articles in digital repositories is increasing. This spectacular growth of repositories makes it rather difficult for researchers to obtain related research papers in response to their queries. The problem becomes worse when a researcher with insufficient knowledge of searching research articles uses these repositories. In the traditional recommendation approaches, the results of the query miss many high-quality papers, in the related work section, which are either published recently or have low citation count. To overcome this problem, there needs to be a solution which considers not only structural relationships between the papers but also inspects the quality of authors publishing those articles. Many research paper recommendation approaches have been implemented which includes collaborative filtering-based, content-based, and citation analysis-based techniques. The collaborative filtering-based approaches primarily use paper-citation matrix for recommendations, whereas the content-based approaches only consider the content of the paper. The citation analysis considers the structure of the network and focuses on papers citing or cited by the paper of interest. It is therefore very difficult for a recommender system to recommend high-quality papers without a hybrid approach that incorporates multiple features, such as citation information and author information. The proposed method creates a multilevel citation and relationship network of authors in which the citation network uses the structural relationship between the papers to extract significant papers, and authors' collaboration network finds key authors from those papers. The papers selected by this hybrid approach are then recommended to the user. The results have shown that our proposed method performs exceedingly well as compared with the state-of-the-art existing systems, such as Google scholar and multilevel simultaneous citation network.

Research Hotspot Prediction and Regular Evolutionary Pattern Identification Based on NSFC Grants Using NMF and Semantic Retrieval

“…Construct a semantic-based research hotspot query. According to the lucene scoring formula (18) and the normalization formula (19), the score values of the keywords and the distributions of the words according to the year are obtained and the hotspot field evolution of the NSFC is identified.…”

“…Norambuena et al [18] proposed a sentiment analysis and opinion mining method that is based on scientific research papers. Waheed et al [19] proposed a hybrid method for the recommendation of scientific research papers and promoted the full use of the papers. Through the analysis and mining of Korean patent information, Lee et al [20] used topic modeling and Latent Dirichlet Allocation (LDA) to identify research opportunities and to predict research hotspots.…”

A Collaborative Approach Toward Scientific Paper Recommendation Using Citation Context

“…They considered both 'cites' and 'cited by' relationships between scientific papers beyond a single level to recommend high quality papers. Waheed et al [34] proposed a hybrid approach that combines multilevel citation networks and authors relationship networks. They identified key authors from their relationship networks to recommend papers to user.…”

A Hybrid Model Based on LFM and BiGRU Toward Research Paper Recommendation

“…Recommendation system based on hybrid recommendation. The hybrid recommendation algorithm usually integrates different recommendation models to complement each other to achieve better recommendation results [18]. Basu et al [19] proposed a method that can simultaneously apply citation information and content information to calculate the similarity between two papers.…”