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A scientific paper recommender system (RS) is very helpful for literature searching in that it (1) helps novice researchers explore their own field and (2) helps experienced researchers explore new fields outside their area of expertise. However, existing RSs usually recommend relevant papers based on users' static interests, i.e., papers they cited in their past publication(s) or reading histories. In this paper, we propose a novel recommendation task based on users' dynamic interests during their paper-writing activity. This dynamism is revealed in (for example) the topic shift while writing the Introduction vs. Related Works section. In solving this task, we developed a new pipeline called " S ection-based S equential S cientific Pa per R ecommendation (S3PaR)", which recommends papers based on the context of the given user's currently written paper section. Our experiments demonstrate that this unique task and our proposed pipeline outperform existing standard RS baselines. [ABSTRACT FROM AUTHOR]

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.

Researchers usually face difficulties in finding scientific papers relevant to their research interests due to increasing growth. Recommender systems emerge as a leading solution to filter valuable items intelligently. Recently, deep learning algorithms, such as convolutional neural network, improved traditional recommendation technologies, for example, the graph‐based or content‐based methods. However, existing graph‐based methods ignore high‐order association between users and items on graphs, and content‐based methods ignore global features of texts for explicit user preferences. Therefore, this paper proposes a Content‐based and knowledge Graph‐based Paper Recommendation method (CGPRec), which uses a two‐layer self‐attention block to obtain global features of texts for more complete explicit user preferences, and proposes an improved graph convolutional network for modeling high‐order associations on the knowledge graph to mine implicit user preferences. And the knowledge graph in this paper is constructed with concept nodes, user nodes, paper nodes, and other meta‐data nodes. Experimental results on a public dataset, CiteULike‐a, and a real application log dataset, AHData, show that our model outperforms compared with baseline methods. [ABSTRACT FROM AUTHOR]

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.

Citation recommendation systems play an important role to alleviate the dilemma that scholar users spend a lot of time and experiences for literature survey. With the burgeoning computational models and open data movement, scientific repository can provide more evidence in support of recommendation. On the one hand, recommenders are applying better algorithms to understand the text of user queries and candidate citations. On the other hand, more types of data such as citation network and co-author relationship are aggregated to enrich the citation contextual information. The available data used for recommendation has been extended from textual content to enriched context. This review is conducted to identify the information and methods used for recommendations recently. We begin by introducing definitions of the task, recommending factors along with the corresponding problems and some application platforms. Then, we classify existing recommenders according to user query types and review representative methods for each type. We also elaborate on different strategies applied in three main stages of citation recommendation. Finally, a few open issues for future investigations are proposed. [ABSTRACT FROM AUTHOR]