Paper Recommendation with Item-Level Collaborative Memory Network

Yu, Mo; Hu, Yue; Li, Xuewei; Zhao, Mankun; Xu, Tianyi; Liu, Hongwei; Xu, Linying; Yu, Ruiguo

doi:10.1007/978-3-030-29551-6_13

Cited by 2 publications

(7 citation statements)

References 11 publications

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“…[109] Graph-based [110] Hybrid [111] Hybrid [117] Hybrid [118] N e t w o r k × [123] Hybrid also utilise historic user-interaction data or descriptions of paper features (see, e.g. Li et al [57] who describe their approach as network-based while using a graph structure, textual components and user profiles) which would render them as either CF or CBF also.…”

Section: Meta Analysismentioning

confidence: 99%

“…Of the observed paper recommendation systems, six were general systems or methods which were only applied on the domain of paper recommendation [3,4,24,60,118,121]. Two were targeting explicit set-based recommendation of publications where only all papers in the set together satisfy users' information needs [60,61], two recommend multiple papers [42,71] (e.g.…”

Section: Current Categorisationmentioning

confidence: 99%

“…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].…”

Section: User Profilementioning

confidence: 99%

“…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].…”

Section: Embeddingmentioning

confidence: 99%

See 3 more Smart Citations

Scientific paper recommendation systems: a literature review of recent publications

Kreutz

Schenkel

2022

Int J Digit Libr

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Scientific writing builds upon already published papers. Manual identification of publications to read, cite or consider as related papers relies on a researcher’s ability to identify fitting keywords or initial papers from which a literature search can be started. The rapidly increasing amount of papers has called for automatic measures to find the desired relevant publications, so-called paper recommendation systems. As the number of publications increases so does the amount of paper recommendation systems. Former literature reviews focused on discussing the general landscape of approaches throughout the years and highlight the main directions. We refrain from this perspective, instead we only consider a comparatively small time frame but analyse it fully. In this literature review we discuss used methods, datasets, evaluations and open challenges encountered in all works first released between January 2019 and October 2021. The goal of this survey is to provide a comprehensive and complete overview of current paper recommendation systems.

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Section: Meta Analysismentioning

confidence: 99%

Section: Current Categorisationmentioning

confidence: 99%

Section: User Profilementioning

confidence: 99%

Section: Embeddingmentioning

confidence: 99%

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Scientific paper recommendation systems: a literature review of recent publications

Kreutz

Schenkel

2022

Int J Digit Libr

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“…In addition, if there were interactions between users and items in implicit collaborative filtering, it was recorded as 1, otherwise 0. However, 1 or 0 did not indicate positive or negative factors between users and items that generated the interaction [35]. According to users' query keywords, CF approaches can effectively recommend papers to users, but these approaches are generally limited by some problems, e.g., the cold start problem and the data sparsity problem [36].…”

Section: Collaborative Filteringmentioning

confidence: 99%

Keywords-Driven and Popularity-Aware Paper Recommendation Based on Undirected Paper Citation Graph

Kou

Yan

2020

Complexity

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Nowadays, scholar recommender systems often recommend academic papers based on users’ personalized retrieval demands. Typically, a recommender system analyzes the keywords typed by a user and then returns his or her preferred papers, in an efficient and economic manner. In practice, one paper often contains partial keywords that a user is interested in. Therefore, the recommender system needs to return the user a set of papers that collectively covers all the queried keywords. However, existing recommender systems only use the exact keyword matching technique for recommendation decisions, while neglecting the correlation relationships among different papers. As a consequence, it may output a set of papers from multiple disciplines that are different from the user’s real research field. In view of this shortcoming, we propose a keyword-driven and popularity-aware paper recommendation approach based on an undirected paper citation graph, named PRkeyword+pop. At last, we conduct large-scale experiments on the real-life Hep-Th dataset to further demonstrate the usefulness and feasibility of PRkeyword+pop. Experimental results prove the advantages of PRkeyword+pop in searching for a set of satisfactory papers compared with other competitive approaches.

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Paper Recommendation with Item-Level Collaborative Memory Network

Cited by 2 publications

References 11 publications

Scientific paper recommendation systems: a literature review of recent publications

Scientific paper recommendation systems: a literature review of recent publications

Keywords-Driven and Popularity-Aware Paper Recommendation Based on Undirected Paper Citation Graph

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