A graph based approach to scientific paper recommendation

Amami, Maha; Faiz, Rim; Stella, Fabio; Pasi, Gabriella

doi:10.1145/3106426.3106479

Cited by 20 publications

(14 citation statements)

References 21 publications

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“…Papers with highest similarity with target paper will be formed as neighbourhood papers. Finally, the cosine similarity of the content will be computed [10], [84]- [86]. By combining the two methods, this system yields superior performance over the classic recommender systems.…”

Section: Hybrid Methods (Hm)mentioning

confidence: 99%

Scientific Paper Recommendation: A Survey

et al. 2019

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Globally, the recommendation services have become important due to the fact that they support e-commerce applications and different research communities. Recommender systems have a large number of applications in many fields, including economic, education, and scientific research. Different empirical studies have shown that the recommender systems are more effective and reliable than the keyword-based search engines for extracting useful knowledge from massive amounts of data. The problem of recommending similar scientific articles in scientific community is called scientific paper recommendation. Scientific paper recommendation aims to recommend new articles or classical articles that match researchers' interests. It has become an attractive area of study since the number of scholarly papers increases exponentially. In this paper, we first introduce the importance and advantages of the paper recommender systems. Second, we review the recommendation algorithms and methods, such as Content-based, collaborative filtering, graph-based, and hybrid methods. Then, we introduce the evaluation methods of different recommender systems. Finally, we summarize the open issues in the paper recommender systems, including cold start, sparsity, scalability, privacy, serendipity, and unified scholarly data standards. The purpose of this survey is to provide comprehensive reviews on the scholarly paper recommendation.

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Section: Hybrid Methods (Hm)mentioning

confidence: 99%

Scientific Paper Recommendation: A Survey

et al. 2019

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“…Graph-based paper recommendation methods firstly construct an academic graph [5]. The papers are treated as vertices, and the paper-paper citation-ships are treated as the edges.…”

Section: Related Work a Scientific Paper Recommendationmentioning

confidence: 99%

“…As far as we know that, existing works generally rely on the citation-ships between papers to make recommendation. Some representative works include graph-based methods [4], [5], collaborative filtering based methods [6], [7]. However, as shown in Figure 1, the real academic information networks are generally heterogeneous graphs [8], which contains multiple types of entities (i.e., author, paper, venue, topic) and relationships (i.e., writing, publishing, collaborating).…”

Section: Introductionmentioning

confidence: 99%

Personalized Scientific Paper Recommendation Based on Heterogeneous Graph Representation

Wang

2019

IEEE Access

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The accelerating rate of scientific publications makes it extremely difficult for researchers to find out the relevant papers and related works. Recommender systems that aim at solving the information overload problem have attracted lots of attention. However, existing paper recommendation works generally rely on the simple citation-ships between papers, which ignore the heterogeneity of the academic graphs. In this paper, we solve the personalized paper recommendation problem in the setting of heterogeneous information networks. A heterogeneous graph representation based recommendation method named HGRec is proposed. First, the author and paper profiles are constructed based on the extracted contents information. Second, we initialize the node vectors by employing the word-embedding technique. Third, we jointly update the node embeddings in the heterogeneous graph by proposing two meta-path based proximity measures. Finally, the paper recommendation is completed by calculating the similarity of the generated author and paper feature vectors. We present experiments on a real academic network, the DBLP network. The comparative results demonstrate the effectiveness of the proposed personalized recommendation approach compared to state-of-the-art methods.

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“…For example, Wang et al proposed a method of combining collaborative filtering with a probabilistic topic model (Wang & Blei, 2011) and using this method to recommend scientific papers. Amami et al proposed a graph-based hybrid academic paper recommendation method (Amami et al, 2017), which combines content analysis method based on probabilistic topic model and collaborative filtering. The experimental results show that the results are satisfactory.…”

Section: Hybrid Filtering Recommendationmentioning

confidence: 99%

A Review on Personalized Academic Paper Recommendation

Li¹,

Zou²

2019

CIS

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With the advent of the era of big data, it has become extremely easy for scientific users to have to access academic papers, which has enhanced their efficiency and capacity to search or browse papers. However, it also faces some problems such as the explosion of the literature or information overwhelming. Many researchers focus on academic paper recommendation service, hoping to help scientific users to find documents more efficiently and recommend interested or potentially interested papers which could assist academic users doing research. Through literature review, this paper make a comprehensive summary of the research on personalized academic papers recommendation, presenting the state-of-art of academic paper recommendation methodologies, pointing out its pros and cons and indicating primary evaluation metrics and popular datasets. Finnaly, we outlook the research trend of personalized academic paper recommendation as a reference for interested researchers.

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A graph based approach to scientific paper recommendation

Cited by 20 publications

References 21 publications

Scientific Paper Recommendation: A Survey

Scientific Paper Recommendation: A Survey

Personalized Scientific Paper Recommendation Based on Heterogeneous Graph Representation

A Review on Personalized Academic Paper Recommendation

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