Scholarly Paper Recommendation via Related Path Analysis in Knowledge Graph

Scientific paper recommendation systems: a literature review of recent publications

“…[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].…”

“…Among the most common methods was their application on papers: in an unspecified representation [30,119], Word2Vec [19,37,44,45,55,104,113], Word2Vec of LDA top words [24,107], Doc2vec [21,28,48,62,63,107], Doc2Vec of word pairs [109], BERT [123] and SBERT [5,19]. Most times these approaches do not mention which part of the paper to use as input but some specifically mention the following parts: titles [37], titles and abstracts [28,45], titles, abstracts and bodies [48], keywords and paper [119].…”

“…Several approaches embed the information in their own model embedding: a heterogeneous information network [5], a two-layer NN [37], a scientific social reference network [41], the TransE model [56], node embeddings [63], paper, author and venue embedding [116], user and item embedding [118], a GRU and association rule mining model [71], a GCN embedding of users [104] and an LSTM model [113].…”

“…Tang et al [104] link words with high tf-idf weights from papers to LOD and then merge this knowledge graph with the user-paper graph. Wang et al [113] construct a knowledge graph consisting of users and papers.…”