Content‐based and knowledge graph‐based paper recommendation: Exploring user preferences with the knowledge graphs for scientific paper recommendation

Abstract

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.

Scientific paper recommendation systems: a literature review of recent publications

“…We refrain from trying to introduce sub-categories for this broad field. Only three approaches evaluate against multiple target values [ 21 , 30 , 104 ]. Six approaches (11.11%) utilise clicks of users, only one approach (1.85%) uses read papers as target value.…”

“…Tang et al [ 104 ] introduce CGPrec, a content-based and knowledge graph-based paper recommendation system. They focus on users’ sparse interaction history with papers and strive to predict papers on which users are likely to click.…”

“…[96] Hybrid [98] content-based [104] Hybrid [106] Graph-based [108] Hybrid [113] Knowledge-aware path recurrent network ×…”

“…We differentiate between profiles derived from user interactions and ones derived from papers. Most user profiles are constructed from users' actual interactions: unspecified historical interaction [30,37,56,57,64,118], the mean of the representation of interacted with papers [19], time decayed interaction behaviour [62], liked papers [69,123], bookmarked papers [84,119], read papers [111,113], rated papers [3,4,110], clicked on papers [24,26,49], categories of clicked papers [1], features of clicked papers [104], tweets [74][75][76], social interactions [65] and explicitly defined topics of interest tags [119].…”

“…To close this gap, a plethora of paper recommendation systems have been proposed recently [37,39,88,104,117]. These systems should fulfil different functions: for junior researchers systems should recommend a broad variety of papers, for senior ones the recommendations should align more with their already established interests [9] or help them discover relevant interdisciplinary research [100].…”