A novel hybrid publication recommendation system using compound information

Abstract

Publication recommendation is an interesting but challenging research problem. Most existing studies only use partial information of papers' contents, reference network or co-author relationship, which leads to an unsatisfied recommendation result. In this study, we propose a novel hybrid publication recommendation approach using compound information which retrieves top-K most relevant papers from a publication depository for a set of user

Scientific paper recommendation systems: a literature review of recent publications

“… When considering the broadest definition of graph-based methods many recent paper recommendation systems tend to belong to the class of hybrid methods. Most of the approaches [ 5 , 46 , 48 , 49 , 57 , 88 , 105 , 117 ] utilise some type of graph structure information as part of the approach which would classify them as graph-based but as they 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.…”

“…For venue-based popularity measures, we found an unspecific reputation notion [ 116 ] as well as incorporation of the impact factor [ 26 , 117 ].…”

“…Yang et al [ 117 ] incorporate the age of papers and impact factors of venues as weights in their citation network-based approach named PubTeller. Papers are clustered by topic, the most popular ones from the clusters most similar to the query terms are recommendation candidates.…”

“…Datasets are grouped by their underlying data source if possible Name A? Used by DBLP + Citations v1 [ 105 ] [ 117 ] DBLP + Citations v8 [ 105 ] [ 63 , 64 ] DBLP + Citations v11 [ 5 ] dblp + IEEE + ACM + Pubmed [ 22 ] DBLP paths [ 42 ] DBLP-Citation-network f. AMiner [ 44 ] dblp [ 57 ] DBLP-REC [ 96 ] dblp + AMiner KG [ 113 ] dblp + AMiner + venue [ 116 ] SPRD_Senior [ 27 ] SPRD [ 101 ] [ 39 , ...…”

“…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].…”