A unified graph model for personalized query-oriented reference paper recommendation

Meng, Fanqi; Gao, Dehong; Li, Wenjie; Sun, Xu; Hou, Yuexian

doi:10.1145/2505515.2507831

Cited by 50 publications

(32 citation statements)

References 14 publications

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“…Baseline 3 (Random Walk (RW) [32]): RW runs on 2layer graph, i.e., the undirected paper citation graph and the built paper-keywords graph. In addition, each query only uses users' entered keywords: q � [0, q W ], and this approach only executes the keywords query of set C. Baseline 4 (Random Walk Restart (RWR) [31]): RWR runs on same 2-layer graph. Furthermore, this approach only executes the query keywords of set C, i.e., q � [0, q W ].…”

Section: Experimental Settingsmentioning

confidence: 99%

“…Currently, the papers' relationships can further reflect the future research trends of paper recommendation, which is mainly because the correlation relationships among papers can indicate the correlation of papers' research contents. For example, Meng et al [31] regarded authors, papers, topics, and keywords as nodes and their relationships as edges, and the approach recommended academic papers by executing the random walk on a fourlayer heterogeneous graph. Furthermore, Gori and Pucci [32] proposed the graph-based PageRank-like recommendation approach that performed the biased random walk on paper citation graphs, and the approach further emphasized on the correlations among citations.…”

Section: Graph Modelmentioning

confidence: 99%

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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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Section: Experimental Settingsmentioning

confidence: 99%

Section: Graph Modelmentioning

confidence: 99%

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

Kou

Yan

2020

Complexity

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“…Recent studies employed graph-based approaches to investigate the reference paper recommendation problem [6][7][8][9][10]. Strohman et al [6] deemed reference paper recommendation as link prediction problem.…”

Section: Graph-based Reference Paper Recommendationmentioning

confidence: 99%

“…Meng et al [9] presented a personalized reference paper recommendation approach, which incorporated different kinds of information, such as content of papers, authorship and citation etc., into a unified graph model. Pan et al [10] proposed an academic paper recommendation approach based on a heterogeneous graph containing various kinds of features.…”

Section: Graph-based Reference Paper Recommendationmentioning

confidence: 99%

Heterogeneous Information Network Embedding based Personalized Query-Focused Astronomy Reference Paper Recommendation

Cai¹,

Han²,

Pan³

et al. 2018

IJCIS

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Fast-growing scientific papers bring the problem of rapidly and accurately finding a list of reference papers for a given manuscript. Reference paper recommendation is an essential technology to overcome this obstacle. In this paper, we study the problem of personalized query-focused astronomy reference paper recommendation and propose a heterogeneous information network embedding based recommendation approach. In particular, we deem query researchers, query text, papers and authors of the papers as vertices and construct a heterogeneous information network based on these vertices. Then we propose a heterogeneous information network embedding (HINE) approach, which simultaneously captures intra-relationships among homogeneous vertices, interrelationships among heterogeneous vertices and correlations between vertices and text contents, to model different types of vertices as vector formats in a unified vector space. The relevance of the query, the papers and the authors of the papers are then measured by the distributed representations. Finally, the papers which have high relevance scores are presented to the researcher as recommendation list. The effectiveness of the proposed HINE based recommendation approach is demonstrated by the recommendation evaluation conducted on the IOP astronomy journal database.

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“…Existing methods for recommending publications can be mapped into three main approaches: content-based [12,14,18], graph-based [10,17,20], and collaborative filtering methods [9,18,19]. Techniques such as PageRank [3] and HITS [7] can also be used to pre-compute the authority scores for publications, which can then be used in conjunction with text similarity to rank documents.…”

Section: Related Workmentioning

confidence: 99%

A Query-oriented Approach for Relevance in Citation Networks

Totti

Mitra

Ouzzani

et al. 2016

Proceedings of the 25th International Conference Companion on World Wide Web - WWW '16 Companion

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Finding a relevant set of publications for a given topic of interest is a challenging problem. We propose a two-stage query-dependent approach for retrieving relevant papers given a keyword-based query. In the first stage, we utilize content similarity to select an initial seed set of publications; we then augment them by citation links weighted with information such as citation context relevance and age-based attenuation. In the second stage, we construct a multi-layer graph that expands the publications subgraph by including links to the authors, venues, and keywords. This allows us to return recommendations that are both highly authoritative, and also textually related to the query. We show that our staged approach gives superior results on three different benchmark query sets.

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A unified graph model for personalized query-oriented reference paper recommendation

Cited by 50 publications

References 14 publications

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

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

Heterogeneous Information Network Embedding based Personalized Query-Focused Astronomy Reference Paper Recommendation

A Query-oriented Approach for Relevance in Citation Networks

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